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{{EthicInfobox\draw<br />
|image=my_location_2.jpg<br />
|caption=Smartphone screen with user's location<br />
|issue=Geolocation disclosure via Location Services software<br />
|value=Privacy, Disclosure, Society<br />
|intent=Positive and malicious<br />
|stakeholders=Various<br />
|legal=Legality depends on intent<br />
|social=Applications are possible<br />
}}<!-- http://www.speakerboxpr.com/wp-content/uploads/2015/09/my_location_image.jpg --><br />
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'''{{initial|S}}martphones''' have been around since the early 1990s, but it was not until the end of the decade that manufacturers began fabricating these devices with [[Wikipedia:Global_Positioning_System|global positioning system]] (GPS) functionality.<ref>Webdesigner Depot: The Evolution of Cell Phone Design Between 1983-2009 http://www.webdesignerdepot.com/2009/05/the-evolution-of-cell-phone-design-between-1983-2009/</ref> Whereas a [[Wikipedia:Mobile_phone|mobile phone]] is a portable device that can make and receive calls over a radio frequency (assuming that the user is within the physical limits of the carrier’s service area), a [[Wikipedia:Smartphone|smartphone]] extends the capabilities of a standard mobile phone by permitting an [[Wikipedia:Operating_system|operating system]] (OS) to be installed on the handset.<ref>Wikipedia: Mobile phone - Introduction https://en.wikipedia.org/wiki/Mobile_phone</ref><ref>Wikipedia: Smartphone - Introduction https://en.wikipedia.org/wiki/Smartphone</ref> The distinction between a mobile phone and smartphone may seem incidental, but it is important when it comes to accurately interpreting their features. Over the years the operating system software on smartphones have become quite elaborate and feature-rich. The integration of a high performance [[Wikipedia:Central_processing_unit|central processing unit]] (CPU), [[Wikipedia:Rendering_(computer_graphics)|graphics engine]], and extremely high pixel density [[Wikipedia:Touchscreen|touchscreen]] display affords customers the ability to do more than ever before with this constantly evolving mobile technology. It is this nexus of software, hardware, and expanding creative possibilities that have precipitated the deluge of [[Wikipedia:Mobile_app|applications]] (or “apps”) and accessories for these devices. As the active [[Wikipedia:Installed_base|user base]] becomes more populated, especially with first-time smartphone users, the need for clear policies, transparent user agreements, and intuitive user interfaces concurrently rises. Besides the obvious priorities among the companies in this competitive market, such as mitigating [[Wikipedia:Switching_barriers|switching costs]] and creating a pleasing overall customer experience, new ethical responsibilities have emerged for their consideration. Arguably, one of the most pressing of these issues is related to the embedded [[Wikipedia:GPS_navigation_device#Privacy_concern|GPS technology]], now commonplace in these devices, that hosts a variety of challenging ethical issues as the market for them becomes increasingly saturated.<br />
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''Note:'' unless otherwise indicated, use of the term “devices” shall represent smartphones in the general sense throughout the following selection.<br><br><br />
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__TOC__<br />
<br />
==Background and History of the Technology==<br />
[[File:BenefonEscTable2.jpg|300px|thumb|right|Benefon's Esc! GPS System]]<br />
The first decidedly full-featured GPS enabled smartphone appeared in 1999. It was called the Esc! and was originally produced by a company called Benefon.<ref>[http://www.webdesignerdepot.com/2009/05/the-evolution-of-cell-phone-design-between-1983-2009/] Retrieved on 2-20-2016</ref> After a series of mergers and acquisitions, the corporation now representing this early entry into the smartphone market is called [[Wikipedia:Twig_Com|Twig Com]] out of Salo, Finland. To this day they remain specialists in the [[Wikipedia:Mobile_telephony|mobile telecommunications]] industry and are known for personal safety and GPS products developed specifically for worker protection, telecare, and asset tracking applications.<ref>Wikipedia: Twig Com - Introduction and History https://en.wikipedia.org/wiki/Twig_Com</ref> Naturally, as the production of the Esc! occurred in Europe, a vast majority of the manufactured devices were sold and distributed there, with only a small fraction of them coming to the United States and elsewhere.<br />
<br />
The extent to which a GPS enabled smartphone can accurately determine location has greatly improved over the succeeding years. There are now 24 dedicated satellites in geosynchronous orbit around the earth that are part of the [[Wikipedia:GPS_satellite_blocks|GPS Satellite Constellation]].<ref>Federal Aviation Administration: GNSS Frequently Asked Questions - GPS https://www.faa.gov/about/office_org/headquarters_offices/ato/service_units/techops/navservices/gnss/faq/gps/</ref> Monitored and maintained by the [[Wikipedia:United_States_Air_Force|United States Air Force]] GPS Systems Wing, the signals communicated by these satellites service the needs of both the civilian population and military. Depending on the device, there are two widely available types of implementations: GPS and [[Wikipedia:Assisted_GPS|A-GPS (assisted)]].<br />
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GPS in its traditional form utilizes [[Wikipedia:Multilateration|multilateration]] to pinpoint a destination. This solution, still relatively unchanged from its original conceptual design, is predicated on the properties of time and distance.<ref>Physics.org: How does GPS work? http://www.physics.org/article-questions.asp?id=55</ref> As a navigation technique, it uses the principle of relative measurement with respect to distance and angle to construct a number of [[Wikipedia:Hyperbolic_function|hyperbolic curves]].<ref>Multilateration: Executive Reference Guide, p. 8 (PDF) http://www.multilateration.com/downloads/MLAT-ADS-B-Reference-Guide.pdf</ref> When overlaid on a defined two-dimensional space, an intersecting point is revealed that represents the origin of the GPS receiver. This “point” is commonly referred to as a “fix” among users of such devices, designating a position on the earth’s surface defined by a pair of geographic coordinates called [[Wikipedia:Geographic_coordinate_system|latitude and longitude]]. Although this approach is sufficient for many situations, the assisted GPS technique can be substituted to accomplish the same objective but with inherent time savings and added precision. Found in most current smartphone applications, A-GPS complements the well established GPS technology with the data signals the device already receives from nearby cell towers and Wi-Fi networks. Essentially adding another layer of information to the mathematical position calculation, this process has improved the ability of these devices to rapidly isolate smartphone users’ locations (i.e. enhancing the [[Wikipedia:Time_to_first_fix|time-to-first-fix]]) as well as sustain their tracking in environments where GPS is known to suffer from the effects of tall buildings or other natural impediments interfering with line of sight to the sky.<ref>Columbia University: Geolocation and Assisted GPS (PDF) http://www.cs.columbia.edu/~drexel/CandExam/Geolocation_assistedGPS.pdf</ref> Additionally, incorporating continuous data delivery over the cellular network results in a significant liberation of space on the devices themselves, allowing users to fill them with other media of more personal relevance.<br />
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Although uncommon, external receivers for some smartphones are still out on the market for commercial purposes. These connect via serial or Bluetooth to Java-enabled phones for interfacing with commercial navigation software.<ref>Wikipedia: GPS navigation device - Mobile phones with GPS capability https://en.wikipedia.org/wiki/GPS_navigation_device</ref><br />
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==Stakeholders==<br />
With an estimated 6.1 billion smartphone users in the world by the 2020, we are on track to exceed basic fixed line phone subscriptions in the coming years.<ref>TechCrunch: 6.1B Smartphone Users Globally By 2020, Overtaking Basic Fixed Phone Subscriptions http://techcrunch.com/2015/06/02/6-1b-smartphone-users-globally-by-2020-overtaking-basic-fixed-phone-subscriptions/</ref> The impending obsolescence of these comparatively “wired” forms of technology, such as home phones, is a foreshadowing of the trend to come as wireless, hands-free, and remote become words that define the next generation of the technology industry. As the smartphone user base swells, so does the potential stakeholder contingents. Currently, when separated at the highest possible level, the principal groups of individuals that stand to gain or lose the most from smartphone technology equipped with GPS facilities during the next five to ten years are:<br />
<br />
*the poor and poverty stricken - individuals relying on their devices for access to food, water, shelter, and other resources -- the basic ingredients of life -- will derive exceptional benefit from having access to location information, especially in underdeveloped and third-world countries;<ref>The New York Times: Fighting Homelessness, One Smartphone at a Time http://www.nytimes.com/2015/04/15/upshot/fighting-homelessness-one-smartphone-at-a-time.html?_r=0</ref><ref>Australian Communications and Media Authority: Homelessness and smartphones (DOC) http://www.acma.gov.au/webwr/_assets/main/lib550060/item_9-homelessness_and_smartphones-acma.docx</ref><br />
*mobile phone users - the demographic using outdated mobile phones will continue to shrink as more people transition to and find value in smartphone technology equipped with location services capable of being embedded on various software platforms to serve a seemingly infinite number of needs;<ref>GSMA Intelligence: From feature phones to smartphones, the road ahead https://gsmaintelligence.com/research/2015/01/from-feature-phones-to-smartphones-the-road-ahead/456/</ref><ref>NewJersey.com: Smartphones to overtake traditional cell phones, become the new 'standard' http://www.nj.com/business/index.ssf/2011/09/smartphones_overtake_feature_p.html</ref><br />
*software developers - the demand for proficient software developers with an understanding of user experience (UX/UI) knowledge to design new and innovative geolocation platforms will expand and more than likely grow concurrently with the smartphone user base;<ref>Application Resource Center from Applause: 5 Things Developers Need To Know About The Future Of The Apps Economy http://arc.applause.com/2015/04/07/apps-economy-developer-insights-and-economics-2015/</ref><ref>Computerworld: Your next job: Mobile app developer? http://www.computerworld.com/article/2509463/app-development/your-next-job--mobile-app-developer-.html</ref><br />
*hardware developers - consistent with [[Wikipedia:Moore%27s_law|Moore’s Law]] and indicative of the history of digital technology to date, we can anticipate the next noteworthy innovation in location servicing on smartphones, including iterative updates to GPS in-device (front-end) or to the supporting infrastructure (backend), to roughly follow the same evolutionary arc (every 1.5 to 2 years);<br />
*[[Social_Networking|social networks]] - location tagging and recommendations are becoming major components of the most popular online social platforms to display user activity;<br />
*advertising and marketing - as a consequence of the massive potential audience to whom firms can display marketing and advertise goods, capitalizing on digital spaces -- especially those facilitating user networking services where location can improve the user experience -- will remain a high priority; and<br />
*various commercial and industrial markets - lucrative returns in the form of [[Wikipedia:Monetization|economic monetization]] through the curation, analysis, and transformation of proprietary data resources will be achieved by those corporations that embrace the future promise of location technology.<br />
<br />
Select societal and industrial stakeholders are evaluated and each is shown to have developed at least one pathway of dependence or critical business priority contingent on the information goods produced by location-based services. Their respective interactions and relevancy to the technology are multifaceted, but the shared characteristic among them all is a commitment to leverage the resulting informational opportunities in constructive ways for giving themselves or their business an edge they wouldn’t have had otherwise. Both the individual and society at large has much to gain from using this technology when it is broadly engaged with in responsible ways (i.e. design can change but relying on -- or expecting, ideally -- that a rational, fair-minded, and [[Wikipedia:Normative#Philosophy|normative value]] approach is the adopted standard); however, this is not to say that it doesn’t come without its latent pitfalls when someone introduces a distorted ideology or chooses to put their immediate concerns ahead of others and sacrifices the health, robustness, and collective security of the system for a misguided and careless motive. Despite the innate breadth and scale over which location-based smartphones have the capacity to effect change, without the integrity of the preceding system related concerns, there will be low incentive on behalf of the system’s existing constituents to endorse its use, and by extension stimulate progress and demand for further improvements and feature enhancements.<br />
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Anticipating the ebbs and flows of the medium outside the five to ten-year timespan previously indicated are too difficult to conceive (see [[Wikipedia:Moore%27s_law|Moore’s Law]]). So for the previous analysis, a relatively narrow future was considered for evaluation.<br />
<br />
==Legislation==<br />
A number of important legal doctrines have materialized from the prevalence of GPS-enabled smartphones. Despite the relative newness of the technology, legal precedents from the past have also managed to maintain their applicability regarding the administration of fundamental rights like privacy, security, and limiting the extent to which the federal government can intervene in citizens’ lives. To govern privacy on the device, with the goal in most circumstances to fortify the personal privacy of the user, restrictions in the form of federal laws have been put in place to guarantee (up to reasonable limits) the security of the owner. The prevalence of terrorist threats and criminal plots around the world, many of which are using smartphones, has demanded stricter legislation in many instances and therefore also opened the door for more governmental reach.<ref>Federal Bureau of Investigation: Are Technology, Privacy, and Public Safety on a Collision Course? (Speech transcript, 10-16-2014) https://www.fbi.gov/news/speeches/going-dark-are-technology-privacy-and-public-safety-on-a-collision-course</ref> Much like how computer hackers can unlawfully acquire control over a remote computer’s webcam or microphone, familiarity with the appropriate methods can grant a third-party access to a smartphone’s GPS, even when it appears to be turned off.<ref>HG.org Legal Resources: What Does the Law Say About Using Someone's Webcam or Computer Microphone to Spy on Them? https://www.hg.org/article.asp?id=31868</ref> As a result, understanding the technical and legal loopholes and comprehending the extent to which others can unlawfully or lawfully (with the proper clearance) penetrate our devices are important for mitigating risk and intelligently navigating the digital landscape.<br />
<br />
===Electronic Communication Privacy Act of 1986 (ECPA)===<br />
Originally adopted to minimize the government’s means of wire tapping phone calls and any other electronic data transmission by a computer-aided medium. It was later amended by the Communications Assistance for Law Enforcement Act (CALEA) of 1994, the USA Patriot Act (including reauthorization acts of 2006), and the FISA Amendments Act (2008).<ref>Wikipedia: Electronic Communications Privacy Act https://en.wikipedia.org/wiki/Electronic_Communications_Privacy_Act</ref><br />
<br />
====New York Times, “1986 Privacy Law if Outrun by the Web”====<br />
<blockquote>“...the Justice Department argued in court that cellphone users had given up the expectation of privacy about their location by voluntarily giving that information to carriers.”<ref>[https://en.wikipedia.org/wiki/Electronic_Communications_Privacy_Act] Retrieved on 2-20-2016</ref></blockquote><br />
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===Communications Assistance for Law Enforcement Act (CALEA)===<br />
Passed during Bill Clinton’s presidency, the purpose of the CALEA was to sanction the Federal government the power of intercepting any telephone traffic by enforcing telecommunications carriers to design their equipment, facilities, and services in a manner conducive for this type of activity. As this was later expanded to include any VoIP and broadband Internet traffic as it saw fit, the implications of acquiring location information became a potentiality.<ref>Wikipedia: Communications Assistance for Law Enforcement Act https://en.wikipedia.org/wiki/Communications_Assistance_for_Law_Enforcement_Act</ref><br />
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===USA Patriot Act===<br />
Federal government and law enforcement have traditionally required warrants for obtaining permission to search or exploit the informational content on a citizen’s personal device (see [https://www.law.cornell.edu/rules/frcrmp/rule_41 Rule 41]); however, with the special powers granted by the [[Wikipedia:Patriot_Act|Patriot Act]], like the controversial [https://www.eff.org/issues/national-security-letters National Security Letter] (NSL) clause ([https://www.law.cornell.edu/uscode/text/18/2709 18 U.S.C. § 2709]), additional authority has been allocated to conduct just this type of activity when national security is believed to be at risk and there exists “[[Wikipedia:Probable_cause|probable cause]]” or “[[Wikipedia:Reasonable_suspicion|reasonable suspicion]].”<ref>Wikipedia: Patriot Act https://en.wikipedia.org/wiki/Patriot_Act</ref><ref>The Atlantic: The Messy Legal Status of Warrantless Cell Phone Location Tracking http://www.theatlantic.com/technology/archive/2015/08/warrantless-cell-phone-location-tracking/400775/</ref> Because the assessment is inherently one-sided with respect to the agencies and individuals within the government who possess ample clearance for carrying out the inquiry, the possibility of limited oversight and no independent review represents an area of ethical concern, or at the very least, a dangerous judicial precedent.<br />
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===Foreign Intelligence Surveillance Act (FISA, 1978) and Amendments Act (2008)===<br />
Widely known to have compromised the privacy of millions of innocent Americans, the [[Wikipedia:Foreign_Intelligence_Surveillance_Act|Foreign Intelligence Surveillance Act]] (or FISA) is the legal grounds on which many of the mass surveillance programs revealed by Edward Snowden are based. As a number of U.S. telecommunication carriers were involved in supporting this operation, most notably AT&T, the opportunity to seize or monitor phone calls, e-mails, or other forms of electronic information and cellular data (ostensibly comprising the location information on some of these devices) was within the government’s grasp and for all intents and purposes, legally condoned.<ref>Wikipedia: Foreign Intelligence Surveillance Act of 1978 Amendments Act of 2008 https://en.wikipedia.org/wiki/Foreign_Intelligence_Surveillance_Act_of_1978_Amendments_Act_of_2008</ref><br />
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===Fourth Amendment===<br />
Contrary to the questionable legal and ethical nuance present under the Patriot Act, the [[Wikipedia:Fourth_Amendment_to_the_United_States_Constitution|Fourth Amendment]] rights of the Constitution, guaranteed to every American citizen, protect the security of United States’ constituency from “unreasonable [[Wikipedia:Search_and_seizure|search and seizure]].”<ref>Wikipedia: Fourth Amendment to the United States Constitution https://en.wikipedia.org/wiki/Fourth_Amendment_to_the_United_States_Constitution</ref> The very obvious overlapping conflict between these two laws has been a source of much debate and protest. Since there is a fine line that qualifies someone as posing a security “risk” to the country, many feel that the allocation of such power is unreasonable for any context, especially one lacking absolute factual omniscience. Whether it’s lack of situational certainty or the introduction of perverse motives at any stage of a warrant or NSL’s approval process, the costs to the privacy of innocent subjects remain a matter of grave importance.<ref>Epic.org: Electronic Privacy Information Center - Locational Privacy https://epic.org/privacy/location_privacy/</ref><br />
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===Third-party Doctrine===<br />
In accordance with United States legal theory, [[Wikipedia:Third-party_doctrine|third-party doctrine]] states that with the voluntary release of personal information to a third-party (e.g. an [[Wikipedia:Internet_service_provider|Internet service provider]]), one forfeits their “reasonable expectation of privacy.”<ref>Wikipedia: Third-party doctrine https://en.wikipedia.org/wiki/Third-party_doctrine</ref> Precedence for the doctrine originated in case of [[Wikipedia:Katz_v._United_States|''Katz v. United States'']] where it was determined that any good knowingly introduced to the public -- informational or otherwise -- is no longer amenable to Fourth Amendment protection.<ref>Wikipedia: ''Katz v. United States'' https://en.wikipedia.org/wiki/Katz_v._United_States</ref> Having been cited in numerous contentious legal cases surrounding the collection of location data and other personal information, it serves to mediate the juncture of government and the individual insofar as what constitutes ownership when the digital world happens to intrinsically confuse the fundamental conception of the term.<br />
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==Notable Cases from the News==<br />
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===''U.S. v. Jones'' (Decided Jan. 23, 2012)===<br />
A [[Wikipedia:Supreme_court|Supreme Court]] ruling banning warrantless GPS search and surveillance by law enforcement through the act of attaching a GPS-enabled device to a vehicle for monitoring its whereabouts.<ref>Wikipedia: ''U.S. v. Jones'' https://en.wikipedia.org/wiki/United_States_v._Jones_(2012)</ref> Although this case didn’t deal with location issues stemming from smartphone use, it set up an expectation for privacy leading into the ''U.S. v. Graham'' court decision occurring less than two months later; conflating a specific behavior and legal ruling on privacy meant that the court had a basis on which to make a more informed judgement, even if only for comparative purposes, with respect to a case dealing with a smartphone and the location information that it received and transmitted.<br />
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===''U.S. v. Graham'' (Decided Mar. 1, 2012)===<br />
A landmark case out of the [[Wikipedia:Maryland_District_Court|Maryland District Court]] reached a verdict resolving that historical, long-term cell-site location information (CSLI) is not protected by rights conferred under the Fourth Amendment.<ref>[http://www.theatlantic.com/technology/archive/2015/08/warrantless-cell-phone-location-tracking/400775/] Retrieved on 2-20-2016</ref> Upholding the plaintiff's argument that the circumstances of the case did not violate any “reasonable [[Wikipedia:Expectation_of_privacy|expectation of privacy]]” by invoking the third-party doctrine, the Defendant’s [[Wikipedia:Motion_to_suppress|Motion to Suppress]] Evidence of historical CSLI was denied.<ref>Wikipedia: ''U.S. v. Graham'' https://en.wikipedia.org/wiki/United_States_v._Graham</ref><br />
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===Apple’s Letter to Customers (February, 2016)===<br />
In an unprecedented move, the [[Wikipedia:Federal_Bureau_of_Investigation|Federal Bureau of Investigation]] (FBI) requested that [[Wikipedia:Apple_Inc.|Apple Inc.]], the well known tech giant responsible for the [[Wikipedia:IPhone|iPhone]], unlock one of their devices (an iPhone 5C) belonging to a shooter from the [[Wikipedia:2015_San_Bernardino_attack|San Bernardino terrorist attack]] that occurred on December 2, 2015 following unsuccessful attempts of their own to break the smartphone’s strong [[Wikipedia:Encryption|encryption]]. Citing the company’s guiding moral principles and fear that acquiescing to these ends would only do more harm than good in the long run, they declined cooperation. In a letter written to their customers and the public at large, [[Wikipedia:Tim_Cook|Tim Cook]] -- Apple’s standing CEO -- said the following:<br />
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<blockquote>“Smartphones, led by iPhone, have become an essential part of our lives. People use them to store an incredible amount of personal information, from our private conversations to our photos, our music, our notes, our calendars and contacts, our financial information and health data, ''even where we have been and where we are going''” (February 16, 2016).<ref>Apple: Customer Letter (2-16-2016) https://www.apple.com/customer-letter/</ref></blockquote><br />
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Since the publication of Cook’s letter on Apple’s website, various other prominent CEOs from the technology industry have endorsed his interpretation and ultimate decision on the matter noting the fact that it could be an unsafe precedent to set.<ref>CNBC: Tech CEOs in support of Apple vs FBI http://www.cnbc.com/2016/02/18/tech-ceos-in-support-of-apple-vs-fbi.html</ref><br />
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==Sources of Positive Utilization==<br />
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===Location Data for Emergency Responders===<br />
In a special addendum to the laws governing the third-party acquisition of location data from smartphones, emergency services are permitted to use this sensitive data for supporting first responders during an emergency situation.{{Citation needed}}<br />
===Smartphone Applications===<br />
The population of smartphone applications is growing by the day. Delivered primarily through Apple’s [[Wikipedia:App_Store_(iOS)|iOS App Store]] (1.5M apps), Android’s [[Wikipedia:Google_Play|Google Play]] (1.6M apps), and the [[Wikipedia:Windows_Phone_Store|Windows Phone Store]] (340K apps), the respective platforms showcase software solutions to many of life’s daily challenges.<ref>Statista: Number of apps available in leading app stores as of July 2015 http://www.statista.com/statistics/276623/number-of-apps-available-in-leading-app-stores/</ref> The stunning growth and liquidity of this market is attracting new capital, individuals, and companies to the promise of quick profits. A relatively inexpensive method for distribution (via one of the respective companies’ digital storefronts) to a tremendous user volume with little overhead to speak of enhances the appeal of the business space.<br />
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A significant fraction of these smartphone apps include those that utilize the real time location of the device. Solving informational problems from navigating rush hour traffic, finding your car in a congested parking lot, to keeping track of your outdoor walks or runs are to the credit of these cunningly designed applications.<ref>Tom's Guide: 10 Best Location Aware Apps http://www.tomsguide.com/us/best-location-aware-apps,review-2405.html</ref> With many sellers offering completely free versions and the option to upgrade to a full-fledged paid variant if desired, the customer's barrier to entry is very low.<br />
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==Ethical Implications and Pitfalls==<br />
<br />
===Companies and Carriers Who Retain Location Data===<br />
Depending on the situation and the immediate policies and laws in question, software companies and telecommunication carriers may be authorized (either independently or through an external source) to store your location information. This is the principal ethical question to consider when it comes to location services and smartphone technology: Are these bodies of power deserving of this authority and to what ends should they be allowed to exercise it? Additional concerns and topics for contemplation include:<br />
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*Default settings (i.e. [[Wikipedia:Opt-out|opt-out]] rather than opt-in)<br />
*[[Wikipedia:End_user|End user]] ignorance regarding safe technology practices<br />
*[[Wikipedia:File_system_permissions|Permissions]] and security audits between a device’s OS and third-party apps to identify weak areas could mitigate the occurrence of unauthorized [[Wikipedia:Exploit_(computer_security)|software exploits]] taking place without the customer’s knowledge<br />
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===Differentiating Public and Private Spaces===<br />
At least in legal terms, the distinction between public and private is an important one. As was the case with ''U.S. v. Graham'' and invocation of the third-party doctrine. Further explication warranted.{{Citation needed}}<br />
===Employer Location Tracking===<br />
Many careers and professional positions require a company supplied smartphone to handle all business related correspondence (e.g. e-mail, text, and voice). As such a device is normally paid for by one’s employer, there have been a handful of newsworthy scenarios where the said employer has assumed the liberty of tracking their staff to ensure proper adherence to company policy and worker productivity.<ref>PBS Newshour: Is your boss tracking your location from your smartphone? http://www.pbs.org/newshour/bb/boss-tracking-location-smartphone/</ref> Infringing upon their privacy, regardless of the underlying legality of the behavior, is something that has the potential to annoy, frustrate, and even enrage the individuals who are affected. Consequently, many feel this issue should be clearly laid out during the hiring process to avoid unexpected revelations and be opened to a public forum for discussion.<br><br />
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==External Links==<br />
{{resource|<br />
*[http://electronics.howstuffworks.com/gps-phone.htm/printable HowStuffWorks: How GPS Phones Work]<br />
*[https://www.propublica.org/article/cellphone-companies-will-share-your-location-data-just-not-with-you ProPublica: Cellphone Companies Will Share Your Location Data - Just Not With You]<br />
}}<br />
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==See Also==<br />
{{resource|<br />
*[[Cellphone surveillance]]<br />
*[[iOS]]<br />
*[[Android]]<br />
}}<br />
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==References==<br />
{{resource|<br />
<references/><br />
}}<br><br />
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[[Category: Information Ethics]]<br />
[[Category: Citations Needed]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Talk:Information_Security&diff=57318Talk:Information Security2016-04-26T17:20:33Z<p>Zzasuwa: Created page with "Clarifying details were necessary for distilling the difference between privacy and security. A level 2 section was added under "Conceptual Overview" to flesh out this point...."</p>
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<div>Clarifying details were necessary for distilling the difference between privacy and security. A level 2 section was added under "Conceptual Overview" to flesh out this point. The "Ethical Implications" section was rather weak and this was expanded on greatly to account for modern concerns over information security. - Z. Zasuwa, 4/26/2016</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Talk:Data_Mining&diff=57315Talk:Data Mining2016-04-26T17:16:10Z<p>Zzasuwa: </p>
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<div>I added extensively to the "Ethical Implications" section as it was definitely needing some extra work. Additionally, I expanded here on various "use cases" where data mining confronts potential ethical ambiguities in society. - Z. Zasuwa, 4/26/2016<br />
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The model used to outline the processes of data mining given in this article is not the only available model given by experts in the field -- it just seems to be an apt model for describing the case for most instances of it. Other uses could consider replacing it if they felt a different model worked better.</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Talk:Ransomware&diff=57314Talk:Ransomware2016-04-26T17:10:49Z<p>Zzasuwa: Created page with "Created this entry as a subpage within the malware category. As ransomware is becoming a greatly discussed topic in society, especially within the technology sector, I thought..."</p>
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<div>Created this entry as a subpage within the malware category. As ransomware is becoming a greatly discussed topic in society, especially within the technology sector, I thought it was pertinent to have a class wiki page that delved into the details of the issue. - Z. Zasuwa, 4/26/2016</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Venmo&diff=57312Venmo2016-04-26T17:02:04Z<p>Zzasuwa: Rewording and clarification for Venmo/Privacy</p>
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|HEIGHT=450<br />
|LOGO=VenmoHeader.png<br />
|SCREENIMAGE=VenmoScreenshot.jpg<br />
|CAPTION="Screenshot of the Venmo application"<br />
|IMAGEURL=https://venmo.com/<br />
|PRODUCT=Mobile Payment Application <br/> Music Sharing Application<br />
|TYPE=Mobile Payment Application<br />
|PLATFORM=[[iOS]] <br/> [[Android (operating system)]] <br/> Web<br />
|SITEURL=https://venmo.com/<br />
|URLTEXT=www.venmo.com<br />
|STATUS=Active<br />
|LAUNCH= 2009<br />
}}<br />
<br />
'''{{initial|V}}enmo''' is a mobile payment application across Android and iOS platforms where users can instantly send and receive money to and from other Venmo users. Structured by either putting money into a Venmo account or linking a bank account/debit card, Venmo revolutionizes the transferring of money quickly.<ref>Venmo-About the Product https://venmo.com/about/product/</ref> Unlike other mobile payment systems such as [[Google Wallet]] or [[Apple Pay]], Venmo only works for peer-to-peer transfers. It is not used to directly purchase items from sellers.<ref> Questions About Security http://abcnews.go.com/Technology/venmo-app-users-raise-questions-security-peer-peer/story?id=29658676</ref> Although Venmo provides a convenient service to many people, it also has its own set of ethical issues regarding security and privacy issues.<br />
<br />
==History==<br />
In 2012, [[Braintree]] acquired Venmo for $26.2 million.<ref>Cash is for Losers http://www.bloomberg.com/bw/articles/2014-11-20/mobile-payment-startup-venmo-is-killing-cash</ref> Then, in 2013, PayPal acquired [[Braintree]] for $800 million in an all-cash deal.<ref>PayPal Acquires Braintree http://techcrunch.com/2013/09/26/paypal-acquires-payments-gateway-braintree-for-800m-in-cash/</ref> In Quarter 2 of 2015, company earnings showed that people sent $1.6 billion over Venmo.<ref> People Sent $1.6 Billion Over Venmo http://thenextweb.com/apps/2015/07/16/people-sent-1-6b-over-venmo-in-q2-2015-more-than-double-this-time-last-year/#gref</ref><br />
<br />
The founders of Venmo are Andrew Kortina and Iqram Magdon-Ismail. The two met at the [[University of Pennsylvania]] in 2001 where they were randomly assigned to be each other's roommates. Throughout senior year and for a few years after graduation, they began working on various projects and launched Venmo in August 2009. However, their original idea was a music startup where people could send a text to a band and then receive an [MP3] via email. The idea for sending payments with technology supposedly came during a night where Magdon-Ismail forgot his wallet and owed Kortina money. The original design was for users to send and receive payments via text message. <ref>13 Fascinating Things We Learned About Payments Company Venmo http://www.businessinsider.com/venmo-origin-story-facts-andrew-kortina-2014-6</ref> This idea eventually evolved into becoming today's version of Venmo, where users can send money using the mobile application or web.<br />
<br />
==Service==<br />
===How it Works===<br />
Users begin to set up their Venmo account and must choose how they want to pay other users. There are three options for this:<br />
*Venmo balance<br />
*Credit/debit card<br />
*U.S. Bank account<br />
<br />
Users can then pay and receive money using their Venmo account and the pay option that they have chosen. Contact will appear in the user's address book and they are able to search for someone by username, phone number or email.<ref name="YouTube App Review">Venmo|App Review https://www.youtube.com/watch?v=QlJwgjN3lCY</ref> They can make a payment by clicking a pen and paper icon that will load a screen titled "New Transaction." The user will then enter the name of the person they are sending it to, the amount of the transaction, and the description of what the transaction is for. Users have the option to pay the person or request a payment from them.<ref>Venmo App Review https://www.youtube.com/watch?v=YEcikNNzOkI</ref> If the user were to send a request for someone to pay them, it would send that other person a notification asking them to make a payment. <ref name="YouTube App Review"/><br />
<br />
It is possible to have the money in a Venmo balance be transferred to a bank account. The user must add a bank account (savings or checking) to their Venmo account. They can then go to their profile and click the "transfer to bank" button. The user can enter the amount they want to cash out. Transfers to a user’s bank account will take about 1 business day. <ref name="YouTube App Review"/><br />
<br />
There is a 3% fee on credit cards and some debit cards, but receiving money through the Venmo app is always free.<ref>Venmo About Fees https://venmo.com/about/fees/</ref><br />
<br />
==Ethical Implications==<br />
Personal information and financial information is protected by Venmo's security systems and data encryption. Financial information is protected on secure servers.<ref> Venmo About Security https://venmo.com/about/security/</ref> Because Venmo is a mobile application, its security goes beyond just the application. Phone security remains an issue because it is the platform for the application. There has been debate about how secure Venmo really is. Unlike other mobile applications, Venmo experiences another level of security due to the fact that it deals with bank accounts and confidential and sensitive information. Venmo does not have a phone line to deal directly with customer issues regarding stolen funds or other security concerns. <ref name="Venmo security"> Venmo Security http://www.slate.com/articles/technology/safety_net/2015/02/venmo_security_it_s_not_as_strong_as_the_company_wants_you_to_think.html</ref> This means that customer that need urgent help may not receive immediate attention because Venmo relies on an email system that is known for being slow to respond.<ref name="security issues">Venmo Security Issues http://www.engadget.com/2015/03/02/venmo-security-issues/</ref> This process is very slow and unfair to Venmo users, especially given the fact that Venmo users transfer over $1.6 billion dollars in just a couple months and has the potential of hundreds of incorrect transactions per month.<ref>Venmo Users Transfer $1.6 Billion in 3 Months http://www.businessadministrationinformation.com/news/venmo-users-transferred-1-6-billion-in-3-months </ref><br />
<br />
===Security===<br />
Although [[PayPal]], Venmo's parent company, has [[two-factor authentication]] for every transaction and password reset, Venmo does not. <ref>Venmo Security and Hacking Threat http://www.theverge.com/2015/2/27/8120983/venmo-security-problem-hacking-theft</ref> This means that anyone with access to applications on someone else's phone could potentially transfer large sums of money through Venmo in an instant. In July 2011, Venmo released an iPhone application update, which added a passcode lock features. Users can now lock their Venmo app with a four-digit PIN or Touch ID (for iPhone 6 users).<ref>Iphone Update Released http://blog.venmo.com/hf2t3h4x98p5e13z82pl8j66ngcmry/iphone-update-released?rq=email%20settings</ref> If the user chooses to create a PIN for their account, they will be asked for their PIN or Touch ID every time they log in or open the app.<ref>Add a Pin https://help.venmo.com/customer/portal/articles/1353616-can-i-add-a-pin-to-my-account</ref> This added security measure serves to provide a little more security for Venmo users. Venmo limits users to $300 transfers per week or $2,900 with identity verification. Although Venmo has these limits there have been instances were users were able to transfer over the $300 limit without identity verification and it went through. That can be dangerous especially if someone else were to get into someone else's Venmo account to send large sums of money. The apps convenience is why so many users are not too worried about anything bad happening to them, which is something Venmo has to realize. Yes, they have a great app that helps a lot of people, but it is their job to also make sure their app is secure. <ref> Cash-free Pay Options http://www.cnet.com/how-to/square-vs-venmo-vs-google-wallet-vs-paypal/ Five Ways to Get People To Pay You Back </ref> <br />
<br />
====E-Hackings====<br />
There have been a few alarming cases of security breaches in Venmo's Platform. One of the victims of these security breaches was a senior at Cal State Long Beach who lives in Westminster, California. The 21 year old was robbed of three thousand dollars and later alerted by his Chase bank account, but not by the app. He claims he was not alerted when the other person logged on to his account, and did not get a notice of the transaction. Since the event, Venmo has updated their policy, as now they send emails to alert you of when a transaction has been completed. <br />
Another victim of hacking was a professional poker player by the name of Moshin Charania. His account was hacked for more than two thousand dollars. He was later reimbursed by Venmo, but the issue of account safety was still brought into question. <ref>Venmo Hackings http://nextshark.com/venmo-hacked/</ref><br />
<br />
[[File:venmowords.jpg|right|250px|thumb|An inappropriate Venmo user's posts.]]<br />
<br />
===Privacy===<br />
Because Venmo also has a social aspect associated to it, it deals with many of the same issues that social media sites deal with. Venmo has a “news feed” where users can see various transactions split into public transactions, friends’ transactions and personal transactions. Venmo has put a feature that allows a user to decide if a payment will be public, private or just for their friends. While this option is helpful for Venmo users to protect their privacy, some users do exploit the system by naming their payments inappropriately and using the public option to catch other people's attention for fun.<ref name="YouTube App Review"/> Venmo does not filter out most inappropriate content, which can be ethically damaging and scarring to innocent users, especially younger ones.<br />
<br />
Venmo also allows users to link with other social media sites like Facebook, Twitter, and Foursquare which would then share their payments and posts on that social media. <ref>Venmo Aims to Make Mobile Payments Social http://www.pcworld.com/article/252261/venmo_aims_to_make_mobile_payments_social.html</ref> This social sharing will increase the privacy issues that Venmo has to deal with because it not only has to contend with the privacy of users on their own application, but now other social media sites are involved as well. Within a Venmo user's social network, the company provides three levels of sharing privacy (public, friends only, or private) that can be toggled on the fly. Being transparent and open about privacy and sharing is a key factor for Venmo as their business deals with finances, which is an intrinsically private matter for most people.<br />
<br />
[[File:vicemo.jpg|right|250px|thumb|What a Venmo newsfeed might look like.]]<br />
<br />
===Case Study about Transparency===<br />
In one case, Chris Grey, a 30-year-old Web developer from New York City saw a notification from [[Chase]] bank that debited his account $2850. He opened his Venmo account to see that his password no longer worked. After resetting his password, he was able to see that a new address was entered under his account and notifications had been disabled. The payment was made to a user he didn't know. Venmo did not notify Grey of any of this suspicious activity. Grey said, "I never got an email that my password had changes, that another email was added to my account, that another device was added to my account, or that a lot of my settings had changed." <ref name="Venmo security"/> Venmo does not notify users when login settings have changed, which can be a major ethical issue when accounts get hacked and the user does not know about it until it is too late.<ref name="security issues"/> To counter this, Venmo allows a user to set email, text, and phone notifications to receive instant updates on when a payment is made through their account. However, the process to correct a hacked or incorrect payment is still relatively slow.<br />
<br />
==See Also==<br />
{{resource|<br />
*[[Mobile Payment]]<br />
*[[Square, Inc.]]<br />
}}<br />
<br />
==References==<br />
{{resource|<br />
<references/><br />
}}</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Ransomware&diff=57307Ransomware2016-04-26T16:45:23Z<p>Zzasuwa: /* Ethical Implications */</p>
<hr />
<div>[[File:Ransomware_2.png|right|thumb|350px|Ransomware is a digital threat that users should be aware of and understand to remain safe online.]]<br />
'''Ransomware''' is a type of malicious software designed for disabling access to a user’s computer system, effectively holding it “ransom”, until a predetermined sum of money is paid.<ref>(2010). ransomware. In Stevenson, A. & Lindberg, C. (Eds.), New Oxford American Dictionary: Oxford University Press. Retrieved 24 Apr. 2016, from http://www.oxfordreference.com.proxy.lib.umich.edu/view/10.1093/acref/9780195392883.001.0001/m_en_us1444048</ref> The unintended result of clicking on an infected browser popup or visiting a compromised website, payment generally takes the form of an anonymous Internet-based currency like Bitcoin. As a rule, victims of ransomware do not share a common demographic or set of characteristics. Anyone who browses the open Web without the proper safeguards in place or neglects to abide by common sense security practices are vulnerable to the effects of this malign software.<br />
<br />
==Variations and History==<br />
[[File:Ransomware_1.jpg|right|thumb|350px|Much like the traditionally familiar dangers of thievery or extortion, ransomware poses similar threats for the networked world.]]<br />
Ransomware can vary in sophistication and implementation but is usually developed to proliferate automatically and penetrate otherwise secure systems by a trojan-like method of attack. The result of a combination of insufficient browser security, a deficient firewall configuration, or general carelessness in the online environment, the end result is the same: temporary re-appropriation of your data via an encryption barrier or restricted access through the display of spam (i.e., pornographic images) until such a time that the user submits and pays a fee for liberating their system of the nefarious software.<ref>Boatman, Kim. Your Security Resource: Beware the Rise of Ransomware. Norton by Symantec. (n.d.). Retrieved 25 Apr. 2016, from https://us.norton.com/yoursecurityresource/detail.jsp?aid=rise_in_ransomware</ref><br />
<br />
===Encrypting ransomware===<br />
The first type of ransomware to hit the Web appeared in 1989 with the [[Wikipedia:AIDS_(Trojan_horse)|"AIDS" trojan]] (also known as "PC Cyborg").<ref>Wikipedia: Ransomware - Encrypting ransomware https://en.wikipedia.org/wiki/Ransomware#Encrypting_ransomware</ref> Following a similar approach of current malware of this type, an alert would be issued that a software license had expired on the user's system and the hard disk would be immediately locked down -- encrypted and rendered inaccessible -- until a money transfer was completed. Still in its infancy at this point, exploiting the gullible and unknowledgeable was clearly a monetary opportunity for those with the necessary programming skills and flexible moral standards for developing robust ransomware exploits.<br />
<br />
Significantly more computationally advanced forms of extortionate ransomware appeared five to six years into the first decade of the twentieth century. With comparatively stronger cryptologic encoding than previous versions, workarounds and fail-safes effectively disappeared. The evolution of [[Wikipedia:Web_2.0|Web 2.0]], [[Wikipedia:Social_media|social media]] platforms, and elevated sharing of online content (files, links, e-mail, etc.) resulted in the more prolific appearance and diffusion of ransomware throughout the wider Internet.<br />
<br />
===Non-encrypting ransomware===<br />
As an alternate class of ransomware, the non-encrypting variety does not assert control over file privileges, but rather forcefully exposes the infected system, and thus the user, to a barrage of unsolicited spam, mostly in the form of crude pornographic images. Appearing a number of years after the first encrypting versions of ransomware hit the Web, the non-encrypting variants also sought to take advantage of unsuspecting and innocent victims who contracted the software trojan through irresponsible browsing habits or subpar digital safeguards. As mentioned above for encrypting ransomware, the only advertised solution for cleansing a computer system of such malware is by paying the specified sum of money. Though this might outwardly appear to be a simple, although costly quick-fix, experts suggest not relinquishing any funds despite the appeal and promise of having your system restored to its previous working order. Often times, despite the pledge by the software to do just that, no steps will be taken to return it to its earlier state.<br />
<br />
==Ethical Implications==<br />
Ethical discussion surrounding the creation, distribution, and ramifications of ransomware is laden with a healthy quantity of mostly one-sided opinion. As the contraction of this malware is unsuspected and without clear warning -- a purposeful and subversive attack on a system and user for purely monetary gain -- many people make quick work of resolving the development of such hostile software and its harmful intents to be ethically unconscionable.<br />
<br />
===Information Ownership===<br />
Arising from the notion of performing unauthorized actions on users' personal data without their consent is the argument of unsanctioned file manipulation. Carefully designed and executed software hacking can permit illegitimate and unconventional access to others' property. Regardless of the potential ignorance on behalf of the user or the ethical frameworks cited, the immediate reaction by the majority when confronted with this issue is one of disgust and disdain. Consequently, a conviction consistent with prevailing [[Wikipedia:Common_law|common law]] (i.e., do not take what is not yours, respect others' privacy, etc.) is exercised by most when invited to address such a problem.<br />
<br />
===Law and Ethics===<br />
The confluence of the legal aspects of computing and the ethical issues revealed by disclosive practices is occurring more frequently than ever before. As many digital behaviors possess borderline issues of legal concern, considerations for the commensurate enforcement of violations that transpire within this domain is a relevant matter for both legislators and the public at large. As laws are a product of the legislature, which is responsible for developing and revising public policy, there are subtle yet direct ethical influences that result from this human involvement insofar as the moral opinion and mental-emotional tendencies between individuals continues to naturally, albeit predictably vary.<br />
<br />
==See Also==<br />
* [[Spam]]<br />
* [[Cyberwarfare]]<br />
* [[Information Assurance/Threats]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Malware]]<br />
[[category: Privacy]]<br />
[[category: Information Assurance]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Ransomware&diff=57247Ransomware2016-04-26T02:48:33Z<p>Zzasuwa: </p>
<hr />
<div>[[File:Ransomware_2.png|right|thumb|350px|Ransomware is a digital threat that users should be aware of and understand to remain safe online.]]<br />
'''Ransomware''' is a type of malicious software designed for disabling access to a user’s computer system, effectively holding it “ransom”, until a predetermined sum of money is paid.<ref>(2010). ransomware. In Stevenson, A. & Lindberg, C. (Eds.), New Oxford American Dictionary: Oxford University Press. Retrieved 24 Apr. 2016, from http://www.oxfordreference.com.proxy.lib.umich.edu/view/10.1093/acref/9780195392883.001.0001/m_en_us1444048</ref> The unintended result of clicking on an infected browser popup or visiting a compromised website, payment generally takes the form of an anonymous Internet-based currency like Bitcoin. As a rule, victims of ransomware do not share a common demographic or set of characteristics. Anyone who browses the open Web without the proper safeguards in place or neglects to abide by common sense security practices are vulnerable to the effects of this malign software.<br />
<br />
==Variations and History==<br />
[[File:Ransomware_1.jpg|right|thumb|350px|Much like the traditionally familiar dangers of thievery or extortion, ransomware poses similar threats for the networked world.]]<br />
Ransomware can vary in sophistication and implementation but is usually developed to proliferate automatically and penetrate otherwise secure systems by a trojan-like method of attack. The result of a combination of insufficient browser security, a deficient firewall configuration, or general carelessness in the online environment, the end result is the same: temporary re-appropriation of your data via an encryption barrier or restricted access through the display of spam (i.e., pornographic images) until such a time that the user submits and pays a fee for liberating their system of the nefarious software.<ref>Boatman, Kim. Your Security Resource: Beware the Rise of Ransomware. Norton by Symantec. (n.d.). Retrieved 25 Apr. 2016, from https://us.norton.com/yoursecurityresource/detail.jsp?aid=rise_in_ransomware</ref><br />
<br />
===Encrypting ransomware===<br />
The first type of ransomware to hit the Web appeared in 1989 with the [[Wikipedia:AIDS_(Trojan_horse)|"AIDS" trojan]] (also known as "PC Cyborg").<ref>Wikipedia: Ransomware - Encrypting ransomware https://en.wikipedia.org/wiki/Ransomware#Encrypting_ransomware</ref> Following a similar approach of current malware of this type, an alert would be issued that a software license had expired on the user's system and the hard disk would be immediately locked down -- encrypted and rendered inaccessible -- until a money transfer was completed. Still in its infancy at this point, exploiting the gullible and unknowledgeable was clearly a monetary opportunity for those with the necessary programming skills and flexible moral standards for developing robust ransomware exploits.<br />
<br />
Significantly more computationally advanced forms of extortionate ransomware appeared five to six years into the first decade of the twentieth century. With comparatively stronger cryptologic encoding than previous versions, workarounds and fail-safes effectively disappeared. The evolution of [[Wikipedia:Web_2.0|Web 2.0]], [[Wikipedia:Social_media|social media]] platforms, and elevated sharing of online content (files, links, e-mail, etc.) resulted in the more prolific appearance and diffusion of ransomware throughout the wider Internet.<br />
<br />
===Non-encrypting ransomware===<br />
As an alternate class of ransomware, the non-encrypting variety does not assert control over file privileges, but rather forcefully exposes the infected system, and thus the user, to a barrage of unsolicited spam, mostly in the form of crude pornographic images. Appearing a number of years after the first encrypting versions of ransomware hit the Web, the non-encrypting variants also sought to take advantage of unsuspecting and innocent victims who contracted the software trojan through irresponsible browsing habits or subpar digital safeguards. As mentioned above for encrypting ransomware, the only advertised solution for cleansing a computer system of such malware is by paying the specified sum of money. Though this might outwardly appear to be a simple, although costly quick-fix, experts suggest not relinquishing any funds despite the appeal and promise of having your system restored to its previous working order. Often times, despite the pledge by the software to do just that, no steps will be taken to return it to its earlier state.<br />
<br />
==Ethical Implications==<br />
Ethical discussion surrounding the creation, distribution, and ramifications of ransomware is laden with a healthy quantity of mostly one-sided opinion. As the contraction of this malware is unsuspected and without clear warning -- a purposeful and subversive attack on a system and user for purely monetary gain -- many people make quick work of resolving the development of such hostile software and its harmful intents to be ethically unconscionable.<br />
<br />
===Information Ownership===<br />
Arising from the notion of performing unauthorized actions on users' personal data without their consent is the argument of unsanctioned file manipulation. Carefully designed and executed software hacking can permit illegitimate and unconventional access to others' property. Regardless of the potential ignorance on behalf of the user or the ethical doctrines cited, the immediate reaction by the majority when confronted with this issue is one of disgust and disdain. Consequently, a conviction consistent with prevailing [[Wikipedia:Common_law|common law]] (i.e., do not take what is not yours, respect others' privacy, etc.) is exercised by most when invited to address such a problem.<br />
<br />
==See Also==<br />
* [[Spam]]<br />
* [[Cyberwarfare]]<br />
* [[Information Assurance/Threats]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Malware]]<br />
[[category: Privacy]]<br />
[[category: Information Assurance]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Ransomware&diff=57245Ransomware2016-04-26T02:42:10Z<p>Zzasuwa: /* Information Ownership */</p>
<hr />
<div>[[File:Ransomware_2.png|right|thumb|350px]]<br />
'''Ransomware''' is a type of malicious software designed for disabling access to a user’s computer system, effectively holding it “ransom”, until a predetermined sum of money is paid.<ref>(2010). ransomware. In Stevenson, A. & Lindberg, C. (Eds.), New Oxford American Dictionary: Oxford University Press. Retrieved 24 Apr. 2016, from http://www.oxfordreference.com.proxy.lib.umich.edu/view/10.1093/acref/9780195392883.001.0001/m_en_us1444048</ref> The unintended result of clicking on an infected browser popup or visiting a compromised website, payment generally takes the form of an anonymous Internet-based currency like Bitcoin. As a rule, victims of ransomware do not share a common demographic or set of characteristics. Anyone who browses the open Web without the proper safeguards in place or neglects to abide by common sense security practices are vulnerable to the effects of this malign software.<br />
<br />
==Variations and History==<br />
[[File:Ransomware_1.jpg|right|thumb|350px]]<br />
Ransomware can vary in sophistication and implementation but is usually developed to proliferate automatically and penetrate otherwise secure systems by a trojan-like method of attack. The result of a combination of insufficient browser security, a deficient firewall configuration, or general carelessness in the online environment, the end result is the same: temporary re-appropriation of your data via an encryption barrier or restricted access through the display of spam (i.e., pornographic images) until such a time that the user submits and pays a fee for liberating their system of the nefarious software.<ref>Boatman, Kim. Your Security Resource: Beware the Rise of Ransomware. Norton by Symantec. (n.d.). Retrieved 25 Apr. 2016, from https://us.norton.com/yoursecurityresource/detail.jsp?aid=rise_in_ransomware</ref><br />
<br />
===Encrypting ransomware===<br />
The first type of ransomware to hit the Web appeared in 1989 with the [[Wikipedia:AIDS_(Trojan_horse)|"AIDS" trojan]] (also known as "PC Cyborg").<ref>Wikipedia: Ransomware - Encrypting ransomware https://en.wikipedia.org/wiki/Ransomware#Encrypting_ransomware</ref> Following a similar approach of current malware of this type, an alert would be issued that a software license had expired on the user's system and the hard disk would be immediately locked down -- encrypted and rendered inaccessible -- until a money transfer was completed. Still in its infancy at this point, exploiting the gullible and unknowledgeable was clearly a monetary opportunity for those with the necessary programming skills and flexible moral standards for developing robust ransomware exploits.<br />
<br />
Significantly more computationally advanced forms of extortionate ransomware appeared five to six years into the first decade of the twentieth century. With comparatively stronger cryptologic encoding than previous versions, workarounds and fail-safes effectively disappeared. The evolution of [[Wikipedia:Web_2.0|Web 2.0]], [[Wikipedia:Social_media|social media]] platforms, and elevated sharing of online content (files, links, e-mail, etc.) resulted in the more prolific appearance and diffusion of ransomware throughout the wider Internet.<br />
<br />
===Non-encrypting ransomware===<br />
As an alternate class of ransomware, the non-encrypting variety does not assert control over file privileges, but rather forcefully exposes the infected system, and thus the user, to a barrage of unsolicited spam, mostly in the form of crude pornographic images. Appearing a number of years after the first encrypting versions of ransomware hit the Web, the non-encrypting variants also sought to take advantage of unsuspecting and innocent victims who contracted the software trojan through irresponsible browsing habits or subpar digital safeguards. As mentioned above for encrypting ransomware, the only advertised solution for cleansing a computer system of such malware is by paying the specified sum of money. Though this might outwardly appear to be a simple, although costly quick-fix, experts suggest not relinquishing any funds despite the appeal and promise of having your system restored to its previous working order. Often times, despite the pledge by the software to do just that, no steps will be taken to return it to its earlier state.<br />
<br />
==Ethical Implications==<br />
Ethical discussion surrounding the creation, distribution, and ramifications of ransomware is laden with a healthy quantity of mostly one-sided opinion. As the contraction of this malware is unsuspected and without clear warning -- a purposeful and subversive attack on a system and user for purely monetary gain -- many people make quick work of resolving the development of such hostile software and its harmful intents to be ethically unconscionable.<br />
<br />
===Information Ownership===<br />
Arising from the notion of performing unauthorized actions on users' personal data without their consent is the argument of unsanctioned file manipulation. Carefully designed and executed software hacking can permit illegitimate and unconventional access to others' property. Regardless of the potential ignorance on behalf of the user or the ethical doctrines cited, the immediate reaction by the majority when confronted with this issue is one of disgust and disdain. Consequently, a conviction consistent with prevailing [[Wikipedia:Common_law|common law]] (i.e., do not take what is not yours, respect others' privacy, etc.) is exercised by most when invited to address such a problem.<br />
<br />
==See Also==<br />
* [[Spam]]<br />
* [[Cyberwarfare]]<br />
* [[Information Assurance/Threats]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Malware]]<br />
[[category: Privacy]]<br />
[[category: Information Assurance]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Ransomware&diff=57242Ransomware2016-04-26T02:40:43Z<p>Zzasuwa: /* Encrypting ransomware */</p>
<hr />
<div>[[File:Ransomware_2.png|right|thumb|350px]]<br />
'''Ransomware''' is a type of malicious software designed for disabling access to a user’s computer system, effectively holding it “ransom”, until a predetermined sum of money is paid.<ref>(2010). ransomware. In Stevenson, A. & Lindberg, C. (Eds.), New Oxford American Dictionary: Oxford University Press. Retrieved 24 Apr. 2016, from http://www.oxfordreference.com.proxy.lib.umich.edu/view/10.1093/acref/9780195392883.001.0001/m_en_us1444048</ref> The unintended result of clicking on an infected browser popup or visiting a compromised website, payment generally takes the form of an anonymous Internet-based currency like Bitcoin. As a rule, victims of ransomware do not share a common demographic or set of characteristics. Anyone who browses the open Web without the proper safeguards in place or neglects to abide by common sense security practices are vulnerable to the effects of this malign software.<br />
<br />
==Variations and History==<br />
[[File:Ransomware_1.jpg|right|thumb|350px]]<br />
Ransomware can vary in sophistication and implementation but is usually developed to proliferate automatically and penetrate otherwise secure systems by a trojan-like method of attack. The result of a combination of insufficient browser security, a deficient firewall configuration, or general carelessness in the online environment, the end result is the same: temporary re-appropriation of your data via an encryption barrier or restricted access through the display of spam (i.e., pornographic images) until such a time that the user submits and pays a fee for liberating their system of the nefarious software.<ref>Boatman, Kim. Your Security Resource: Beware the Rise of Ransomware. Norton by Symantec. (n.d.). Retrieved 25 Apr. 2016, from https://us.norton.com/yoursecurityresource/detail.jsp?aid=rise_in_ransomware</ref><br />
<br />
===Encrypting ransomware===<br />
The first type of ransomware to hit the Web appeared in 1989 with the [[Wikipedia:AIDS_(Trojan_horse)|"AIDS" trojan]] (also known as "PC Cyborg").<ref>Wikipedia: Ransomware - Encrypting ransomware https://en.wikipedia.org/wiki/Ransomware#Encrypting_ransomware</ref> Following a similar approach of current malware of this type, an alert would be issued that a software license had expired on the user's system and the hard disk would be immediately locked down -- encrypted and rendered inaccessible -- until a money transfer was completed. Still in its infancy at this point, exploiting the gullible and unknowledgeable was clearly a monetary opportunity for those with the necessary programming skills and flexible moral standards for developing robust ransomware exploits.<br />
<br />
Significantly more computationally advanced forms of extortionate ransomware appeared five to six years into the first decade of the twentieth century. With comparatively stronger cryptologic encoding than previous versions, workarounds and fail-safes effectively disappeared. The evolution of [[Wikipedia:Web_2.0|Web 2.0]], [[Wikipedia:Social_media|social media]] platforms, and elevated sharing of online content (files, links, e-mail, etc.) resulted in the more prolific appearance and diffusion of ransomware throughout the wider Internet.<br />
<br />
===Non-encrypting ransomware===<br />
As an alternate class of ransomware, the non-encrypting variety does not assert control over file privileges, but rather forcefully exposes the infected system, and thus the user, to a barrage of unsolicited spam, mostly in the form of crude pornographic images. Appearing a number of years after the first encrypting versions of ransomware hit the Web, the non-encrypting variants also sought to take advantage of unsuspecting and innocent victims who contracted the software trojan through irresponsible browsing habits or subpar digital safeguards. As mentioned above for encrypting ransomware, the only advertised solution for cleansing a computer system of such malware is by paying the specified sum of money. Though this might outwardly appear to be a simple, although costly quick-fix, experts suggest not relinquishing any funds despite the appeal and promise of having your system restored to its previous working order. Often times, despite the pledge by the software to do just that, no steps will be taken to return it to its earlier state.<br />
<br />
==Ethical Implications==<br />
Ethical discussion surrounding the creation, distribution, and ramifications of ransomware is laden with a healthy quantity of mostly one-sided opinion. As the contraction of this malware is unsuspected and without clear warning -- a purposeful and subversive attack on a system and user for purely monetary gain -- many people make quick work of resolving the development of such hostile software and its harmful intents to be ethically unconscionable.<br />
<br />
===Information Ownership===<br />
Arising from the notion of performing unauthorized actions on users' personal data without their consent is the argument of unsanctioned file manipulation. Carefully designed and executed software hacking can permit illegitimate and unconventional access to others' property. Regardless of the potential ignorance on behalf of the user or the ethical doctrines cited, the immediate reaction by the majority when confronted with this issue is one of disgust and disdain. Consequently, a conviction consistent with prevailing common law (i.e., do not take what is not yours, respect others' privacy, etc.) is exercised by most when invited to address such a problem.<br />
<br />
==See Also==<br />
* [[Spam]]<br />
* [[Cyberwarfare]]<br />
* [[Information Assurance/Threats]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Malware]]<br />
[[category: Privacy]]<br />
[[category: Information Assurance]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Ransomware&diff=57239Ransomware2016-04-26T02:35:24Z<p>Zzasuwa: /* Ethical Implications */</p>
<hr />
<div>[[File:Ransomware_2.png|right|thumb|350px]]<br />
'''Ransomware''' is a type of malicious software designed for disabling access to a user’s computer system, effectively holding it “ransom”, until a predetermined sum of money is paid.<ref>(2010). ransomware. In Stevenson, A. & Lindberg, C. (Eds.), New Oxford American Dictionary: Oxford University Press. Retrieved 24 Apr. 2016, from http://www.oxfordreference.com.proxy.lib.umich.edu/view/10.1093/acref/9780195392883.001.0001/m_en_us1444048</ref> The unintended result of clicking on an infected browser popup or visiting a compromised website, payment generally takes the form of an anonymous Internet-based currency like Bitcoin. As a rule, victims of ransomware do not share a common demographic or set of characteristics. Anyone who browses the open Web without the proper safeguards in place or neglects to abide by common sense security practices are vulnerable to the effects of this malign software.<br />
<br />
==Variations and History==<br />
[[File:Ransomware_1.jpg|right|thumb|350px]]<br />
Ransomware can vary in sophistication and implementation but is usually developed to proliferate automatically and penetrate otherwise secure systems by a trojan-like method of attack. The result of a combination of insufficient browser security, a deficient firewall configuration, or general carelessness in the online environment, the end result is the same: temporary re-appropriation of your data via an encryption barrier or restricted access through the display of spam (i.e., pornographic images) until such a time that the user submits and pays a fee for liberating their system of the nefarious software.<ref>Boatman, Kim. Your Security Resource: Beware the Rise of Ransomware. Norton by Symantec. (n.d.). Retrieved 25 Apr. 2016, from https://us.norton.com/yoursecurityresource/detail.jsp?aid=rise_in_ransomware</ref><br />
<br />
===Encrypting ransomware===<br />
The first type of ransomware to hit the Web appeared in 1989 with the "AIDS" trojan (also known as "PC Cyborg").<ref>Wikipedia: Ransomware - Encrypting ransomware https://en.wikipedia.org/wiki/Ransomware#Encrypting_ransomware</ref> Following a similar approach of current malware of this type, an alert would be issued that a software license had expired on the user's system and the hard disk would be immediately locked down -- encrypted and rendered inaccessible -- until a money transfer was completed. Still in its infancy at this point, exploiting the gullible and unknowledgeable was clearly a monetary opportunity for those with the necessary programming skills and flexible moral standards for developing robust ransomware exploits.<br />
<br />
Significantly more computationally advanced forms of extortionate ransomware appeared five to six years into the first decade of the twentieth century. With comparatively stronger cryptologic encoding than previous versions, workarounds and fail-safes effectively disappeared. The evolution of Web 2.0, social media platforms, and elevated sharing of online content (files, links, e-mail, etc.) resulted in the more prolific appearance and diffusion of ransomware throughout the wider Internet.<br />
<br />
===Non-encrypting ransomware===<br />
As an alternate class of ransomware, the non-encrypting variety does not assert control over file privileges, but rather forcefully exposes the infected system, and thus the user, to a barrage of unsolicited spam, mostly in the form of crude pornographic images. Appearing a number of years after the first encrypting versions of ransomware hit the Web, the non-encrypting variants also sought to take advantage of unsuspecting and innocent victims who contracted the software trojan through irresponsible browsing habits or subpar digital safeguards. As mentioned above for encrypting ransomware, the only advertised solution for cleansing a computer system of such malware is by paying the specified sum of money. Though this might outwardly appear to be a simple, although costly quick-fix, experts suggest not relinquishing any funds despite the appeal and promise of having your system restored to its previous working order. Often times, despite the pledge by the software to do just that, no steps will be taken to return it to its earlier state.<br />
<br />
==Ethical Implications==<br />
Ethical discussion surrounding the creation, distribution, and ramifications of ransomware is laden with a healthy quantity of mostly one-sided opinion. As the contraction of this malware is unsuspected and without clear warning -- a purposeful and subversive attack on a system and user for purely monetary gain -- many people make quick work of resolving the development of such hostile software and its harmful intents to be ethically unconscionable.<br />
<br />
===Information Ownership===<br />
Arising from the notion of performing unauthorized actions on users' personal data without their consent is the argument of unsanctioned file manipulation. Carefully designed and executed software hacking can permit illegitimate and unconventional access to others' property. Regardless of the potential ignorance on behalf of the user or the ethical doctrines cited, the immediate reaction by the majority when confronted with this issue is one of disgust and disdain. Consequently, a conviction consistent with prevailing common law (i.e., do not take what is not yours, respect others' privacy, etc.) is exercised by most when invited to address such a problem.<br />
<br />
==See Also==<br />
* [[Spam]]<br />
* [[Cyberwarfare]]<br />
* [[Information Assurance/Threats]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Malware]]<br />
[[category: Privacy]]<br />
[[category: Information Assurance]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Ransomware&diff=57230Ransomware2016-04-26T02:01:09Z<p>Zzasuwa: /* Non-encrypting ransomware */</p>
<hr />
<div>[[File:Ransomware_2.png|right|thumb|350px]]<br />
'''Ransomware''' is a type of malicious software designed for disabling access to a user’s computer system, effectively holding it “ransom”, until a predetermined sum of money is paid.<ref>(2010). ransomware. In Stevenson, A. & Lindberg, C. (Eds.), New Oxford American Dictionary: Oxford University Press. Retrieved 24 Apr. 2016, from http://www.oxfordreference.com.proxy.lib.umich.edu/view/10.1093/acref/9780195392883.001.0001/m_en_us1444048</ref> The unintended result of clicking on an infected browser popup or visiting a compromised website, payment generally takes the form of an anonymous Internet-based currency like Bitcoin. As a rule, victims of ransomware do not share a common demographic or set of characteristics. Anyone who browses the open Web without the proper safeguards in place or neglects to abide by common sense security practices are vulnerable to the effects of this malign software.<br />
<br />
==Variations and History==<br />
[[File:Ransomware_1.jpg|right|thumb|350px]]<br />
Ransomware can vary in sophistication and implementation but is usually developed to proliferate automatically and penetrate otherwise secure systems by a trojan-like method of attack. The result of a combination of insufficient browser security, a deficient firewall configuration, or general carelessness in the online environment, the end result is the same: temporary re-appropriation of your data via an encryption barrier or restricted access through the display of spam (i.e., pornographic images) until such a time that the user submits and pays a fee for liberating their system of the nefarious software.<ref>Boatman, Kim. Your Security Resource: Beware the Rise of Ransomware. Norton by Symantec. (n.d.). Retrieved 25 Apr. 2016, from https://us.norton.com/yoursecurityresource/detail.jsp?aid=rise_in_ransomware</ref><br />
<br />
===Encrypting ransomware===<br />
The first type of ransomware to hit the Web appeared in 1989 with the "AIDS" trojan (also known as "PC Cyborg").<ref>Wikipedia: Ransomware - Encrypting ransomware https://en.wikipedia.org/wiki/Ransomware#Encrypting_ransomware</ref> Following a similar approach of current malware of this type, an alert would be issued that a software license had expired on the user's system and the hard disk would be immediately locked down -- encrypted and rendered inaccessible -- until a money transfer was completed. Still in its infancy at this point, exploiting the gullible and unknowledgeable was clearly a monetary opportunity for those with the necessary programming skills and flexible moral standards for developing robust ransomware exploits.<br />
<br />
Significantly more computationally advanced forms of extortionate ransomware appeared five to six years into the first decade of the twentieth century. With comparatively stronger cryptologic encoding than previous versions, workarounds and fail-safes effectively disappeared. The evolution of Web 2.0, social media platforms, and elevated sharing of online content (files, links, e-mail, etc.) resulted in the more prolific appearance and diffusion of ransomware throughout the wider Internet.<br />
<br />
===Non-encrypting ransomware===<br />
As an alternate class of ransomware, the non-encrypting variety does not assert control over file privileges, but rather forcefully exposes the infected system, and thus the user, to a barrage of unsolicited spam, mostly in the form of crude pornographic images. Appearing a number of years after the first encrypting versions of ransomware hit the Web, the non-encrypting variants also sought to take advantage of unsuspecting and innocent victims who contracted the software trojan through irresponsible browsing habits or subpar digital safeguards. As mentioned above for encrypting ransomware, the only advertised solution for cleansing a computer system of such malware is by paying the specified sum of money. Though this might outwardly appear to be a simple, although costly quick-fix, experts suggest not relinquishing any funds despite the appeal and promise of having your system restored to its previous working order. Often times, despite the pledge by the software to do just that, no steps will be taken to return it to its earlier state.<br />
<br />
==Ethical Implications==<br />
<br />
===Ethics Subheading===<br />
<br />
==See Also==<br />
* [[Spam]]<br />
* [[Cyberwarfare]]<br />
* [[Information Assurance/Threats]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Malware]]<br />
[[category: Privacy]]<br />
[[category: Information Assurance]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Ransomware&diff=57223Ransomware2016-04-26T01:46:46Z<p>Zzasuwa: /* Non-encrypting ransomware */</p>
<hr />
<div>[[File:Ransomware_2.png|right|thumb|350px]]<br />
'''Ransomware''' is a type of malicious software designed for disabling access to a user’s computer system, effectively holding it “ransom”, until a predetermined sum of money is paid.<ref>(2010). ransomware. In Stevenson, A. & Lindberg, C. (Eds.), New Oxford American Dictionary: Oxford University Press. Retrieved 24 Apr. 2016, from http://www.oxfordreference.com.proxy.lib.umich.edu/view/10.1093/acref/9780195392883.001.0001/m_en_us1444048</ref> The unintended result of clicking on an infected browser popup or visiting a compromised website, payment generally takes the form of an anonymous Internet-based currency like Bitcoin. As a rule, victims of ransomware do not share a common demographic or set of characteristics. Anyone who browses the open Web without the proper safeguards in place or neglects to abide by common sense security practices are vulnerable to the effects of this malign software.<br />
<br />
==Variations and History==<br />
[[File:Ransomware_1.jpg|right|thumb|350px]]<br />
Ransomware can vary in sophistication and implementation but is usually developed to proliferate automatically and penetrate otherwise secure systems by a trojan-like method of attack. The result of a combination of insufficient browser security, a deficient firewall configuration, or general carelessness in the online environment, the end result is the same: temporary re-appropriation of your data via an encryption barrier or restricted access through the display of spam (i.e., pornographic images) until such a time that the user submits and pays a fee for liberating their system of the nefarious software.<ref>Boatman, Kim. Your Security Resource: Beware the Rise of Ransomware. Norton by Symantec. (n.d.). Retrieved 25 Apr. 2016, from https://us.norton.com/yoursecurityresource/detail.jsp?aid=rise_in_ransomware</ref><br />
<br />
===Encrypting ransomware===<br />
The first type of ransomware to hit the Web appeared in 1989 with the "AIDS" trojan (also known as "PC Cyborg").<ref>Wikipedia: Ransomware - Encrypting ransomware https://en.wikipedia.org/wiki/Ransomware#Encrypting_ransomware</ref> Following a similar approach of current malware of this type, an alert would be issued that a software license had expired on the user's system and the hard disk would be immediately locked down -- encrypted and rendered inaccessible -- until a money transfer was completed. Still in its infancy at this point, exploiting the gullible and unknowledgeable was clearly a monetary opportunity for those with the necessary programming skills and flexible moral standards for developing robust ransomware exploits.<br />
<br />
Significantly more computationally advanced forms of extortionate ransomware appeared five to six years into the first decade of the twentieth century. With comparatively stronger cryptologic encoding than previous versions, workarounds and fail-safes effectively disappeared. The evolution of Web 2.0, social media platforms, and elevated sharing of online content (files, links, e-mail, etc.) resulted in the more prolific appearance and diffusion of ransomware throughout the wider Internet.<br />
<br />
===Non-encrypting ransomware===<br />
As an alternate class of ransomware, the non-encrypting variety does not assert control over file privileges, but rather forcefully exposes the infected system, and thus the user, to a barrage of unsolicited spam, mostly in the form of crude pornographic images. Appearing a number of years after the first encrypting versions of ransomware hit the Web, the non-encrypting variants also sought to take advantage of unsuspecting and innocent victims who contracted the software trojan through irresponsible browsing habits or subpar digital safeguards.<br />
<br />
==Ethical Implications==<br />
<br />
===Ethics Subheading===<br />
<br />
==See Also==<br />
* [[Spam]]<br />
* [[Cyberwarfare]]<br />
* [[Information Assurance/Threats]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Malware]]<br />
[[category: Privacy]]<br />
[[category: Information Assurance]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Ransomware&diff=57217Ransomware2016-04-26T01:23:43Z<p>Zzasuwa: Image added to "Variations and History"</p>
<hr />
<div>[[File:Ransomware_2.png|right|thumb|350px]]<br />
'''Ransomware''' is a type of malicious software designed for disabling access to a user’s computer system, effectively holding it “ransom”, until a predetermined sum of money is paid.<ref>(2010). ransomware. In Stevenson, A. & Lindberg, C. (Eds.), New Oxford American Dictionary: Oxford University Press. Retrieved 24 Apr. 2016, from http://www.oxfordreference.com.proxy.lib.umich.edu/view/10.1093/acref/9780195392883.001.0001/m_en_us1444048</ref> The unintended result of clicking on an infected browser popup or visiting a compromised website, payment generally takes the form of an anonymous Internet-based currency like Bitcoin. As a rule, victims of ransomware do not share a common demographic or set of characteristics. Anyone who browses the open Web without the proper safeguards in place or neglects to abide by common sense security practices are vulnerable to the effects of this malign software.<br />
<br />
==Variations and History==<br />
[[File:Ransomware_1.jpg|right|thumb|350px]]<br />
Ransomware can vary in sophistication and implementation but is usually developed to proliferate automatically and penetrate otherwise secure systems by a trojan-like method of attack. The result of a combination of insufficient browser security, a deficient firewall configuration, or general carelessness in the online environment, the end result is the same: temporary re-appropriation of your data via an encryption barrier or restricted access through the display of spam (i.e., pornographic images) until such a time that the user submits and pays a fee for liberating their system of the nefarious software.<ref>Boatman, Kim. Your Security Resource: Beware the Rise of Ransomware. Norton by Symantec. (n.d.). Retrieved 25 Apr. 2016, from https://us.norton.com/yoursecurityresource/detail.jsp?aid=rise_in_ransomware</ref><br />
<br />
===Encrypting ransomware===<br />
The first type of ransomware to hit the Web appeared in 1989 with the "AIDS" trojan (also known as "PC Cyborg").<ref>Wikipedia: Ransomware - Encrypting ransomware https://en.wikipedia.org/wiki/Ransomware#Encrypting_ransomware</ref> Following a similar approach of current malware of this type, an alert would be issued that a software license had expired on the user's system and the hard disk would be immediately locked down -- encrypted and rendered inaccessible -- until a money transfer was completed. Still in its infancy at this point, exploiting the gullible and unknowledgeable was clearly a monetary opportunity for those with the necessary programming skills and flexible moral standards for developing robust ransomware exploits.<br />
<br />
Significantly more computationally advanced forms of extortionate ransomware appeared five to six years into the first decade of the twentieth century. With comparatively stronger cryptologic encoding than previous versions, workarounds and fail-safes effectively disappeared. The evolution of Web 2.0, social media platforms, and elevated sharing of online content (files, links, e-mail, etc.) resulted in the more prolific appearance and diffusion of ransomware throughout the wider Internet.<br />
<br />
===Non-encrypting ransomware===<br />
<br />
==Ethical Implications==<br />
<br />
===Ethics Subheading===<br />
<br />
==See Also==<br />
* [[Spam]]<br />
* [[Cyberwarfare]]<br />
* [[Information Assurance/Threats]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Malware]]<br />
[[category: Privacy]]<br />
[[category: Information Assurance]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Ransomware&diff=57215Ransomware2016-04-26T01:21:16Z<p>Zzasuwa: </p>
<hr />
<div>[[File:Ransomware_2.png|right|thumb|350px]]<br />
'''Ransomware''' is a type of malicious software designed for disabling access to a user’s computer system, effectively holding it “ransom”, until a predetermined sum of money is paid.<ref>(2010). ransomware. In Stevenson, A. & Lindberg, C. (Eds.), New Oxford American Dictionary: Oxford University Press. Retrieved 24 Apr. 2016, from http://www.oxfordreference.com.proxy.lib.umich.edu/view/10.1093/acref/9780195392883.001.0001/m_en_us1444048</ref> The unintended result of clicking on an infected browser popup or visiting a compromised website, payment generally takes the form of an anonymous Internet-based currency like Bitcoin. As a rule, victims of ransomware do not share a common demographic or set of characteristics. Anyone who browses the open Web without the proper safeguards in place or neglects to abide by common sense security practices are vulnerable to the effects of this malign software.<br />
<br />
==Variations and History==<br />
Ransomware can vary in sophistication and implementation but is usually developed to proliferate automatically and penetrate otherwise secure systems by a trojan-like method of attack. The result of a combination of insufficient browser security, a deficient firewall configuration, or general carelessness in the online environment, the end result is the same: temporary re-appropriation of your data via an encryption barrier or restricted access through the display of spam (i.e., pornographic images) until such a time that the user submits and pays a fee for liberating their system of the nefarious software.<ref>Boatman, Kim. Your Security Resource: Beware the Rise of Ransomware. Norton by Symantec. (n.d.). Retrieved 25 Apr. 2016, from https://us.norton.com/yoursecurityresource/detail.jsp?aid=rise_in_ransomware</ref><br />
<br />
===Encrypting ransomware===<br />
The first type of ransomware to hit the Web appeared in 1989 with the "AIDS" trojan (also known as "PC Cyborg").<ref>Wikipedia: Ransomware - Encrypting ransomware https://en.wikipedia.org/wiki/Ransomware#Encrypting_ransomware</ref> Following a similar approach of current malware of this type, an alert would be issued that a software license had expired on the user's system and the hard disk would be immediately locked down -- encrypted and rendered inaccessible -- until a money transfer was completed. Still in its infancy at this point, exploiting the gullible and unknowledgeable was clearly a monetary opportunity for those with the necessary programming skills and flexible moral standards for developing robust ransomware exploits.<br />
<br />
Significantly more computationally advanced forms of extortionate ransomware appeared five to six years into the first decade of the twentieth century. With comparatively stronger cryptologic encoding than previous versions, workarounds and fail-safes effectively disappeared. The evolution of Web 2.0, social media platforms, and elevated sharing of online content (files, links, e-mail, etc.) resulted in the more prolific appearance and diffusion of ransomware throughout the wider Internet.<br />
<br />
===Non-encrypting ransomware===<br />
<br />
==Ethical Implications==<br />
<br />
===Ethics Subheading===<br />
<br />
==See Also==<br />
* [[Spam]]<br />
* [[Cyberwarfare]]<br />
* [[Information Assurance/Threats]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Malware]]<br />
[[category: Privacy]]<br />
[[category: Information Assurance]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Ransomware&diff=57214Ransomware2016-04-26T01:18:46Z<p>Zzasuwa: /* Heading #1 */</p>
<hr />
<div>[[File:Ransomware_2.png|right|thumb|350px]]<br />
'''Ransomware''' is a type of malicious software designed for disabling access to a user’s computer system, effectively holding it “ransom”, until a predetermined sum of money is paid.<ref>(2010). ransomware. In Stevenson, A. & Lindberg, C. (Eds.), New Oxford American Dictionary: Oxford University Press. Retrieved 24 Apr. 2016, from http://www.oxfordreference.com.proxy.lib.umich.edu/view/10.1093/acref/9780195392883.001.0001/m_en_us1444048</ref> The unintended result of clicking on an infected browser popup or visiting a compromised website, payment generally takes the form of an anonymous Internet-based currency like Bitcoin. As a rule, victims of ransomware do not share a common demographic or set of characteristics. Anyone who browses the open Web without the proper safeguards in place or neglects to abide by common sense security practices are vulnerable to the effects of this malign software.<br />
<br />
==Variations and History==<br />
Ransomware can vary in sophistication and implementation but is usually developed to proliferate automatically and penetrate otherwise secure systems by a trojan-like method of attack. The result of a combination of insufficient browser security, a deficient firewall configuration, or general carelessness in the online environment, the end result is the same: temporary re-appropriation of your data via an encryption barrier or restricted access through the display of spam (i.e., pornographic images) until such a time that the user submits and pays a fee for liberating their system of the nefarious software.<ref>Boatman, Kim. Your Security Resource: Beware the Rise of Ransomware. Norton by Symantec. (n.d.). Retrieved 25 Apr. 2016, from https://us.norton.com/yoursecurityresource/detail.jsp?aid=rise_in_ransomware</ref><br />
<br />
===Encrypting ransomware===<br />
The first type of ransomware to hit the Web appeared in 1989 with the "AIDS" trojan (also known as "PC Cyborg").<ref>Wikipedia: Ransomware - Encrypting ransomware https://en.wikipedia.org/wiki/Ransomware#Encrypting_ransomware</ref> Following a similar approach of current malware of this type, an alert would be issued that a software license had expired on the user's system and the hard disk would be immediately locked down -- encrypted and rendered inaccessible -- until a money transfer was completed. Still in its infancy at this point, exploiting the gullible and unknowledgeable was clearly a monetary opportunity for those with the necessary programming skills and flexible moral standards for developing robust ransomware exploits.<br />
<br />
Significantly more computationally advanced forms of extortionate ransomware appeared five to six years into the first decade of the twentieth century. With comparatively stronger cryptologic encoding than previous versions, workarounds and fail-safes effectively disappeared. The evolution of Web 2.0, social media platforms, and elevated sharing of online content (files, links, e-mail, etc.) resulted in the more prolific appearance and diffusion of ransomware throughout the wider Internet.<br />
<br />
===Non-encrypting ransomware===<br />
<br />
==Heading #2==<br />
<br />
===Subheading #2===<br />
<br />
==Ethical Implications==<br />
<br />
===Ethics Subheading===<br />
<br />
==See Also==<br />
* [[Spam]]<br />
* [[Cyberwarfare]]<br />
* [[Information Assurance/Threats]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Malware]]<br />
[[category: Privacy]]<br />
[[category: Information Assurance]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Ransomware&diff=57202Ransomware2016-04-26T00:09:25Z<p>Zzasuwa: Added introduction</p>
<hr />
<div>[[File:Ransomware_2.png|right|thumb|350px]]<br />
'''Ransomware''' is a type of malicious software designed for disabling access to a user’s computer system, effectively holding it “ransom”, until a predetermined sum of money is paid.<ref>(2010). ransomware. In Stevenson, A. & Lindberg, C. (Eds.), New Oxford American Dictionary: Oxford University Press. Retrieved 24 Apr. 2016, from http://www.oxfordreference.com.proxy.lib.umich.edu/view/10.1093/acref/9780195392883.001.0001/m_en_us1444048</ref> The unintended result of clicking on an infected browser popup or visiting a compromised website, payment generally takes the form of an anonymous Internet-based currency like Bitcoin. As a rule, victims of ransomware do not share a common demographic or set of characteristics. Anyone who browses the open Web without the proper safeguards in place or neglects to abide by common sense security practices are vulnerable to the effects of this malign software.<br />
<br />
==Heading #1==<br />
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===Subheading #1===<br />
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==Heading #2==<br />
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===Subheading #2===<br />
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==Ethical Implications==<br />
<br />
===Ethics Subheading===<br />
<br />
==See Also==<br />
* [[Spam]]<br />
* [[Cyberwarfare]]<br />
* [[Information Assurance/Threats]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Malware]]<br />
[[category: Privacy]]<br />
[[category: Information Assurance]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Ransomware&diff=57025Ransomware2016-04-24T18:17:48Z<p>Zzasuwa: /* See Also */</p>
<hr />
<div>[[File:Ransomware_2.png|right|thumb|350px]]<br />
'''Ransomware''' is ...<br />
<br />
==Heading #1==<br />
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===Subheading #1===<br />
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==Heading #2==<br />
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===Subheading #2===<br />
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==Ethical Implications==<br />
<br />
===Ethics Subheading===<br />
<br />
==See Also==<br />
* [[Spam]]<br />
* [[Cyberwarfare]]<br />
* [[Information Assurance/Threats]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Malware]]<br />
[[category: Privacy]]<br />
[[category: Information Assurance]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Ransomware&diff=57024Ransomware2016-04-24T18:14:03Z<p>Zzasuwa: /* See Also */</p>
<hr />
<div>[[File:Ransomware_2.png|right|thumb|350px]]<br />
'''Ransomware''' is ...<br />
<br />
==Heading #1==<br />
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===Subheading #1===<br />
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==Heading #2==<br />
<br />
===Subheading #2===<br />
<br />
==Ethical Implications==<br />
<br />
===Ethics Subheading===<br />
<br />
==See Also==<br />
* [[Information Assurance/Threats]]<br />
* [[Spam]]<br />
* [[Cyberwarfare]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Malware]]<br />
[[category: Privacy]]<br />
[[category: Information Assurance]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Ransomware&diff=57023Ransomware2016-04-24T18:13:10Z<p>Zzasuwa: /* References */</p>
<hr />
<div>[[File:Ransomware_2.png|right|thumb|350px]]<br />
'''Ransomware''' is ...<br />
<br />
==Heading #1==<br />
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===Subheading #1===<br />
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==Heading #2==<br />
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===Subheading #2===<br />
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==Ethical Implications==<br />
<br />
===Ethics Subheading===<br />
<br />
==See Also==<br />
* [[Information_Assurance/Threats|Information Assurance/Threats]]<br />
* [[Spam]]<br />
* [[Cyberwarfare]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Malware]]<br />
[[category: Privacy]]<br />
[[category: Information Assurance]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Ransomware&diff=57022Ransomware2016-04-24T18:07:55Z<p>Zzasuwa: /* See Also */</p>
<hr />
<div>[[File:Ransomware_2.png|right|thumb|350px]]<br />
'''Ransomware''' is ...<br />
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==Heading #1==<br />
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===Subheading #1===<br />
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==Heading #2==<br />
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===Subheading #2===<br />
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==Ethical Implications==<br />
<br />
===Ethics Subheading===<br />
<br />
==See Also==<br />
* [[Information_Assurance/Threats|Information Assurance/Threats]]<br />
* [[Spam]]<br />
* [[Cyberwarfare]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Virtual Environments, Concerns, & Issues]]<br />
[[category: Information Ethics]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Ransomware&diff=57021Ransomware2016-04-24T18:07:09Z<p>Zzasuwa: /* See Also */</p>
<hr />
<div>[[File:Ransomware_2.png|right|thumb|350px]]<br />
'''Ransomware''' is ...<br />
<br />
==Heading #1==<br />
<br />
===Subheading #1===<br />
<br />
==Heading #2==<br />
<br />
===Subheading #2===<br />
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==Ethical Implications==<br />
<br />
===Ethics Subheading===<br />
<br />
==See Also==<br />
* [[Information_Assurance/Threats|Information Assurance/Threats]]<br />
* [[Malware]]<br />
* [[Spam]]<br />
* [[Cyberwarfare]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Virtual Environments, Concerns, & Issues]]<br />
[[category: Information Ethics]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Topics&diff=57020Topics2016-04-24T18:05:46Z<p>Zzasuwa: Added ransomware topic</p>
<hr />
<div>http://si410ethics11.projects.si.umich.edu/images/topics.png<br />
<br />
''Please add your newly created pages to this list in alphabetical order, and remember to surround with the appropriate MediaWiki syntax (i.e.:'' <nowiki>*[[your page]]</nowiki>'').''<br />
<br />
== ToolKit ==<br />
<br><br />
{|style="margin: 0em auto 0em auto"<br />
|{{shadowBox|boxHeight=100|boxWidth=740}}<br />
{|style="margin: 8px auto 0px auto" align="center" border="0px"<br />
| width="100px" | [[File:HelpingHand.jpg|80x80px|link=Topics/Boost|Boost]]<br />
| width="100px" | [[File:Working.jpg|80x80px|link=:Category:Action Needed|Action Needed]]<br />
| width="100px" | [[File:Seedling.jpg|80x80px|link=Topics/seed articles|Seed Articles]]<br />
| width="100px" | [[File:Schedule.png|80x80px|link=:Category:Out of Date|'''Out of Date Pages''']]<br />
| width="100px" | [[File:Dictionary.png|80x80px|link=:Category:Definitions|Definitions]]<br />
| width="100px" | [[File:Talk.png|80x80px|link=Topics/AboutWikiStandard|Wiki Terminology Standardization]]<br />
| width="100px" | [[File:Templates.png|80x80px|link=Topics/UserTemplates|Templates for Pages]]<br />
| width="100px" | [[File:Garbage.png|80x80px|link=:Category:IncinerateTrash|Marked Pages for Deletion]]<br />
|-<br />
| [[Topics/Boost|'''Wiki<br>Productivity Tools''']]<br />
| [[:Category:Action Needed|'''Action Needed''']]<br />
| [[Topics/seed articles|'''Seed Articles''']]<br />
| [[:Category:Out of Date|<br>'''Out of Date Pages''']]<br />
| [[:Category:Definitions|'''Definitions''']]<br />
| [[Topics/AboutWikiStandard|'''Standardizing Terminology''']]<br />
| [[Topics/UserTemplates|'''Infoboxes and Templates''']]<br />
| [[:Category:IncinerateTrash|'''Pages Marked for Deletion''']]<br />
|}{{endBox}}<br />
|}<br />
__NOTOC__<br />
<br><br />
<br />
<br><br />
== Gold Star Articles ==<br />
*[[:Category:GoldStar]]<br />
<br/><br />
<br />
== Portals ==<br />
<br><br />
*[[:Portal:Life on Digital Worlds|Life on Digital Worlds]]<br />
<br><br />
<br />
== Categories ==<br />
<br><br />
{| style="width:400px;"<br />
! width="250"|Category<br />
! style="width:150px;text-align:center"|Number of Pages<br />
|-<br />
|[[:Category:Action Needed|Action Needed]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Action Needed}}<br />
|-<br />
|[[:Category:Blogging|Blogging]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Blogging}}<br />
|-<br />
|[[:Category:Censorship|Censorship]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Censorship}}<br />
|-<br />
|[[:Category:Citations Needed|Citations Needed]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Citations Needed}}<br />
|-<br />
|[[:Category:Computer Simulation|Computer Simulation]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Computer Simulation}}<br />
|-<br />
|[[:Category:Concepts|Concepts]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Concepts}}<br />
|-<br />
|[[:Category:Corporations|Corporations]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Corporations}}<br />
|-<br />
|[[:Category:Cyberpunk (genre)|Cyberpunk]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Cyberpunk (genre)}}<br />
|-<br />
|[[:Category:Hardware|Hardware]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Hardware}}<br />
|-<br />
|[[:Category:Information Assurance|Information Assurance]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Information Assurance}}<br />
|-<br />
|[[:Category:Information Ethics|Information Ethics]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Information Ethics}}<br />
|-<br />
|[[:Category:Internet slang|Internet Slang]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Internet slang}}<br />
|-<br />
|[[:Category:Malware|Malware]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Malware}}<br />
|-<br />
|[[:Category:Media Content|Media Content]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Media Content}}<br />
|-<br />
|[[:Category:Missing Information|Missing Information]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Missing Information}}<br />
|-<br />
|[[:Category:Music|Music]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Music}}<br />
|-<br />
|[[:Category:Open Source Projects|Open Source Projects]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Open Source Projects}}<br />
|-<br />
|[[:Category:Organizations|Organizations]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Organizations}}<br />
|-<br />
|[[:Category:Out of Date|Out of Date]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Out of Date}}<br />
|-<br />
|[[:Category:People|People]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:People}}<br />
|-<br />
|[[:Category:Piracy|Piracy]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Piracy}}<br />
|-<br />
|[[:Category:Politics|Politics]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Politics}}<br />
|-<br />
|[[:Category:Portals|Portals]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Portals}}<br />
|-<br />
|[[:Category:Privacy|Privacy]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Privacy}}<br />
|-<br />
|[[:Category:Services|Services]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Services}}<br />
|-<br />
|[[:Category:Social Networking|Social Networking]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Social Networking}}<br />
|-<br />
|[[:Category:Software|Software]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Software}}<br />
|-<br />
|[[:Category:Sports|Sports]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Sports}}<br />
|-<br />
|[[:Category:Video Games|Video Games]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Video Games}}<br />
|-<br />
|[[:Category:Virtual Environments, Concerns, & Issues|Virtual Environments]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Virtual Environments, Concerns, & Issues}}<br />
|-<br />
|[[:Category:Websites|Websites]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Websites}}<br />
|}<br />
<br><br />
<br />
== Topics ==<br />
<br><br />
{{Section-Menu}}<br />
{{Section|||1}}<br />
=== # ===<br />
----<br />
*[[4chan]]<br />
*[[4shared]]<br />
*[[8tracks]]<br />
*[[9GAG]]<br />
=== A ===<br />
----<br />
*[[Actor-Network Theory]]<br />
*[[Adblocking]]<br />
*[[AdverGaming]]<br />
*[[Amazon.com]]<br />
*''the'' [[The Amy Boyer Case|Amy Boyer Case]]<br />
*[[Android]]<br />
*[[Angry Birds]]<br />
*'''''{{Section||[[Anonymous|{{Define|Anonymous}}]]|Anonymous}}'''''<br />
**[[Anonymous Behavior in Virtual Environments|Behavior in Virtual Environment]]<br />
**[[Anonymous (group)|Group]]<br />
*[[APIs]]<br />
*[[Artificial Intelligence and Technology]]<br />
*[[Autonomous Systems]]<br />
*[[Autonomous vehicles]]<br />
*[[Avatar]]<br />
<br />
=== B ===<br />
----<br />
*[[Banality of Simulated Evil]]<br />
*[[Bandcamp]]<br />
*[[Barstool Sports]]<br />
*[[Bartle Test]]<br />
*[[Battlestar Galactica (2004 TV Series)]]<br />
*[[Beer Pong HD]]<br />
*[[Big Data: "Direct Marketing Metrics and Baseline Analysis in The Obama 2012 Campaign"]]<br />
*[[Biobanking]]<br />
*[[BioShock]]<br />
*[[BioWare]]<br />
*[[Bitcoin]]<br />
*[[BitTorrent]]<br />
*[[Blizzard Entertainment]]<br />
*[[Border Gateway Protocol]]<br />
*[[BuzzFeed]]<br />
<br />
=== C ===<br />
----<br />
*[[Call of Duty]]<br />
*[[Carrier IQ]]<br />
*[[Catfishing]]<br />
*[[Cats]]<br />
*[[CEIU Thesis]]<br />
*[[Cellphone surveillance]]<br />
*[[Censorship]]<br />
*[[Chatroulette]]<br />
*[[Cheating]]<br />
*[[Circumventing Internet Censorship]]<br />
*[[Citizendium]]<br />
*[[Civilization]]<br />
*[[Clash of Clans]]<br />
*'''''{{Section||[[Cloud|{{Define|Cloud}}]]|Cloud}}'''''<br />
**[[Cloud Computing|Computing]]<br />
**[[Cloud Security|Security]]<br />
*[[Clueful Chatting]]<br />
*[[Common Sense Media]]<br />
*[[Conficker Worm]]<br />
*[[Conservative Entertainment Complex]]<br />
*[[Cookies]]<br />
*[[Cortana]]<br />
*[[Craigslist]]<br />
*[[Creative Commons]]<br />
*[[Crowdsourcing]]<br />
*'''''{{Section||[[Cyber|{{Define|Cyber}} ]]|Cyber}}''''' {{Relation|overlaps with|Online|#Online}} {{Relation||Internet|#Internet}} {{Relation||Virtual|#Virtual}}<br />
**[[Cyberbullying|Bullying]] {{Relation|cases of|David Thorne|#D}}<br />
**[[Cyberculture|Culture]]<br />
**[[Cyberlaw|Law]]<br />
**[[Cybersex|Sex]]<br />
**''Sex in'' [[Online Dating#Sex|Online Dating]]<br />
**[[Cyberstalking|Stalking]] {{Relation|use of crowdsourcing|Human Flesh Search|#H}} {{Relation||Tiayna.cn|#T}} {{Relation|cases of|Amy Boyer|#A}}<br />
**[[Cyber Command (USCYBERCOM)|USCYBERCOM]]<br />
**[[Cyberwarfare|Warfare]]<br />
<br />
=== D ===<br />
----<br />
*'''''{{Section||[[Data|{{Define|Data}}]]|Data}}''''' {{Relation|overlaps with|Information|#Information}}<br />
**[[Data aggregation and public information|Aggregation of Public Information]]<br />
**[[Data Aggregation Online|Aggregation Online]]<br />
**[[Data Mining|Mining]]<br />
*[[David Thorne]]<br />
*[[DC]]<br />
*[[Detroit Events Center]]<br />
*[[Deus Ex (Series)]]<br />
**[[Deus Ex: Human Revolution]]<br />
*[[Diablo (Franchise)]]<br />
**[[Diablo II]]<br />
**[[Diablo III]]<br />
*[[Diaspora]]<br />
*''Digital''<br />
**[[Digital Divide|Divide]]<br />
**[[Digital DJing|DJing]]<br />
**[[Digital Photography|Photography]] {{Relation|ethical issues|Phototruth|#P}} {{Relation||Photo Editing|#P}}<br />
**[[Digital Piracy|Piracy]]<br />
**[[Digital Rights Management|Rights Management]]<br />
*[[Domain Name System]]<br />
*[[Downloadable Content in Video Games]]<br />
*[[Dropbox]]<br />
*[[Drupal]]<br />
*[[drope]]<br />
<br />
=== E ===<br />
----<br />
*[[eBay]]<br />
*[[Edward Castronova]]<br />
*[[Edward H. Spence]]<br />
*[[Edward Snowden]]<br />
*[[Eggdrop Bots and Botnet]]<br />
*[[Elder Scrolls]]<br />
*[[Electronic Arts]]<br />
*[[Electric Sheep]]<br />
*''Electronic''<br />
**[[Electronic Health Records|Health Records]]<br />
**[[Electronic Sports|Sports]]<br />
**[[Frontier Foundation]]<br />
*[[Emerging Media]]<br />
*''Ethics''<br />
**''in'' [[Ethics in Computer & Video Games|Computer & Video Games]]<br />
**''in'' [[Ethics in Hacking|Hacking]]<br />
**''of'' [[Information Ethics|Information]]<br />
*[[Etsy]]<br />
*[[Experience Project]]<br />
<br />
=== F ===<br />
----<br />
*''Facebook''<br />
**[[Facebook|Company]]<br />
**[[Facebook Privacy Policy|Privacy Policy]]<br />
**[[Data Mining and Manipulation]]<br />
*[[FaceTime]]<br />
*[[File Sharing]]<br />
*[[Flame Malware]]<br />
*[[Flaming]]<br />
*[[Foodporn]]<br />
*[[Foodspotting]]<br />
*[[Foursquare]]<br />
*[[Funeral for Serenity]]<br />
<br />
=== G ===<br />
----<br />
*[[Galaxy S3]]<br />
*[[Game Addiction]]<br />
*[[Gattaca]]<br />
*[[Generative]]<br />
*[[Genovese Syndrome]]<br />
*[[Geographic Information Systems]]<br />
*[[Ghost in the Shell (series)]]<br />
*[[Ghost Writing Online]]<br />
*[[Girls Around Me]]<br />
*[[GLANSER]]<br />
*''Google''<br />
**[[Google|Company]]<br />
**[[Google Books|Books]]<br />
**[[Google Glass| Google Glass]]<br />
**[[Google Street View|Street View]]<br />
*[[Grand Theft Auto IV]]<br />
*[[Griefing]]<br />
=== H ===<br />
----<br />
*[[Hackers]]<br />
*[[Health Informatics]]<br />
*[[Her (film) (2013)]]<br />
*[[Her Interactive]]<br />
*[[Herman Tavani]]<br />
*[[Hulu]]<br />
*[[Human Flesh Search]] {{Relation|related to|Tianya.cn|#T}}<br />
*[["Human out of the Loop" Military Systems]]<br />
<br />
=== I ===<br />
----<br />
*[[id Software]]<br />
*[[IDF social media|I.D.F.'s use of social media]]<br />
*[[Imgur]]<br />
*[[Infamous (series)]]<br />
*[[Informatics]]<br />
*'''''{{Section||[[Information|{{Define|Information}}]]|Information}}''''' {{Relation|overlaps with|Data|#Data}}<br />
**[[Information Assurance|Assurance]]<br />
**[[Information Ethics|Ethics]]<br />
**[[Information Freedom|Ethics]]<br />
**[[Information Graphics|Graphics]]<br />
**[[Information Integrity|Integrity]]<br />
**[[Information Security|Security]]<br />
**[[Information Transparency|Transparency]]<br />
*[[Infosphere]]<br />
*[[Instagram]]<br />
*[[Intellectual Property]]<br />
*[[International Society for Ethics and Information Technology]]<br />
*'''''{{Section||[[Internet]]|Internet}}''''' {{Relation|overlaps with|Cyber|#Cyber}} {{Relation||Online|#Online}} {{Relation||Virtual|#Virtual}}<br />
**[[Internet Archive|Archive]]<br />
**[[Internet Censorship in Hong Kong|Censorship in Hong Kong]]<br />
**''in'' [[Circumventing Internet Censorship|Circumventing Censorhip]]<br />
**[[Internet Control|Control]]<br />
**[[Internet Forum|Forum]]<br />
**[[Internet meme|Meme]]<br />
*[[iOS]]<br />
*[[iTunes Store]]<br />
<br />
=== J ===<br />
----<br />
*[[James H. Moor]]<br />
*[[John Weckert]]<br />
*[[Julian Dibbell]]<br />
<br />
=== K ===<br />
----<br />
*[[Kathleen Wallace]]<br />
*[[Kay Mathiesen]]<br />
*[[Kim Dotcom]]<br />
*[[Kickstarter]]<br />
*[[Kind of Bloop]]<br />
*[[Knowledge Discovery in Databases]]<br />
<br />
=== L ===<br />
----<br />
*[[LambdaMOO]]<br />
*[[Larry Page]]<br />
*[[Lawrence Lessig]]<br />
*[[League of Legends]]<br />
*[[LikeALittle]]<br />
*[[Limewire]]<br />
*[[Line (Application)]]<br />
*[[LinkedIn]]<br />
*[[Lookbook.nu]]<br />
*[[Luciano Floridi]]<br />
<br />
=== M ===<br />
----<br />
*[[MapleStory]]<br />
*[[Mashup]]<br />
*[[Mass Effect]]<br />
*''the'' [[The Matrix|Matrix]]<br />
*[[Internet meme|Meme]]<br />
*[[Matteo Turilli]]<br />
*[[Mechanical Turk]]<br />
*[[Megaupload]]<br />
*[[Mia Consalvo]]<br />
*[[Miguel Sicart]]<br />
*[[Minecraft]]<br />
*[[MMORPGs]]<br />
*[[Mods]]<br />
*[[Morris Worm]]<br />
*[[Mortal Kombat]]<br />
*[[MyBuys]]<br />
*[[myg0t]]<br />
*[[Myspace]]<br />
<br />
=== N ===<br />
----<br />
*[[Napster]]<br />
*[[National Security Agency]]<br />
*[[NCAA Football (Video Game Series)]]<br />
*[[Need For Speed (Video Game Series)]]<br />
*[[Netflix]]<br />
*[[Norbert Wiener]]<br />
*[[Nymwars]]<br />
<br />
=== O ===<br />
----<br />
*'''''{{Section||[[Online]]|Online}}''''' {{Relation|overlaps with|Cyber|#Cyber}} {{Relation||Internet|#Internet}} {{Relation||Virtual|#Virtual}}<br />
**[[Cyberbullying|Bullying]] ''in Cyberspace''<br />
**[[Online Cheating|Cheating]]<br />
**[[Online Dating|Dating]]<br />
**[[Online Identity|Identity]]<br />
**[[Online Identity Theft|Identity Theft]]<br />
**[[Online Reputation Systems|Reputation Systems]]<br />
**''Sex in'' [[Online Dating#Sex|Online Dating]]<br />
**[[Online shopping|Shopping]]<br />
**[[Cyberstalking|Stalking]] ''in CyberSpace''<br />
*''the'' [[The Open Internet|Open Internet]]<br />
*[[Open Source Software]]<br />
*[[OSI Network Model]]<br />
<br />
=== P ===<br />
----<br />
*[[Pandora]]<br />
*[[Parody]]<br />
*[[Patent]]<br />
*[[Penetration Testing (PenTesting)]]<br />
*[[Periscope]]<br />
*[[Philip Brey]]<br />
*''Photo'' {{Relation|issues of|Digital Photography|#D}}<br />
**[[Photo Editing|Editing]]<br />
**[[Phototruth|Truth]]<br />
*[[Pinterest]]<br />
*[[The Pirate Bay|Pirate Bay]]<br />
*[[Plagiarism (Snapper)]]<br />
*[[Planking]]<br />
*[[PokerStars]]<br />
*[[Policy Vacuums]]<br />
*[[Pornography]]<br />
*[[Portal 2]]<br />
*[[Prezi]]<br />
*'''''{{Section||[[Privacy]]|Privacy}}'''''<br />
**[[Facebook Privacy Policy|Facebook Policy]]<br />
**''in'' [[Privacy in the Online Environment|Online Enviornment]]<br />
**''in'' [[Privacy in Social Networking|Social Networking]]<br />
*[[Pro-Ana Forums]]<br />
*[[Protect IP Act]]<br />
*[[Punishments in Virtual Environments]]<br />
<br />
=== R ===<br />
----<br />
*[[Radio-frequency Identification]]<br />
*[[Ransomware]]<br />
*[[Raph Koster]]<br />
*[[Real Money Trade]]<br />
*[[Recommender Systems]]<br />
*[[Reddit]]<br />
*[[Richard Stallman]]<br />
*[[RIP Trolling]]<br />
*[[Rockmelt]]<br />
*[[Romantically Apocalyptic]]<br />
*[[Root Name Server Denial of Service Attacks]]<br />
*[[Information Assurance/Threats|RootKit (Malware)]]<br />
*[[Rubbish]]<br />
<br />
=== S ===<br />
----<br />
*[[Sampling (hip hop)]]<br />
*[[Sharing Subscription Services]]<br />
*''Sims''<br />
**[[The Sims 3|The Sims 3]]<br />
**[[The Sims Online|The Sims Online]]<br />
*[[Siri]]<br />
*[[Smartphones (Location Services)]]<br />
*[[Soccer & FIFA]]<br />
*'''''{{Section||[[Social]]|Social}}'''''<br />
**[[Social Engineering|Engineering]]<br />
**[[Social Media in Sports|Media in Sports]]<br />
**[[Social Networking|Networking]]<br />
**[[Social Networking Services|Networking Services]] {{Relation|for sites|Facebook|Facebook}} {{Relation||Tianya.cn|Tianya.cn}} {{Relation||Twitter|Twitter}} {{Relation||Tumblr|Tumblr}}<br />
*[[Source]]<br />
*[[Snapchat]]<br />
*[[Spam]]<br />
*[[Spoof]]<br />
*[[Spotify]]<br />
*[[Stages in technological revolution]]<br />
*[[Starcraft II]]<br />
*[[Statistical Modeling]]<br />
*[[Steam]]<br />
*[[Stingray]]<br />
*[[Stop Online Piracy Act]]<br />
*[[Student-Athlete Social Media Monitoring]]<br />
*[[StumbleUpon]]<br />
*[[Stuxnet Trojan]] {{Relation|type of|Worm|#W}} {{Relation|utilizes|Rootkit|#R}}<br />
<br />
=== T ===<br />
----<br />
*[[Targeted Advertising (Online)]]<br />
*[[Team Fortress 2]]<br />
*[[Technological Singularity]]<br />
*[[Technology in Fitness and Health]]<br />
*[[Telepresence]]<br />
*[[Think Tech Labs]]<br />
*[[Thomas M. Powers]]<br />
*[[Information Assurance/Threats|Threats]]<br />
*[[TIA|Total Information Awareness]]<br />
*[[Tianya.cn]]<br />
*[[Tim Berners-Lee]]<br />
*[[Tinder]]<br />
*[[Tor]]<br />
*[[Transhumanism]]<br />
*[[Information Assurance/Threats|Trojan (Malware)]]<br />
*[[Troll]]<br />
*[[Trust]]<br />
*[[Tumblr]]<br />
*[[Twitter]]<br />
*[[Twitch.tv]]<br />
<br />
=== U ===<br />
----<br />
*[[Uber]]<br />
*[[Uniqueness Debate]]<br />
*[[Ubiquitous Computing]]<br />
<br />
=== V ===<br />
----<br />
*[[Valve]]<br />
*[[Venmo]]<br />
*'''''{{Section||[[Virtual]]|Virtual}}''''' {{Relation|overlaps with|Cyber|#Cyber}} {{Relation||Internet|#Internet}} {{Relation||Online|#Online}}<br />
**[[Virtual Behavior in Online Role Playing Games|Behavior in Online Role Playing Games]]<br />
**''Bullying in'' [[Cyberbullying|Cyberspace]]<br />
**[[Virtual Child Pornography|Child Pornography]]<br />
**[[Virtual Community|Community]]<br />
**[[Virtual Crimes and Punishments|Crimes and Punishments]]<br />
** ''Dating ''[[Online Dating#Virtual_Dating|Online]]<br />
**[[Virtual Dating Simulations|Dating Simulations]]<br />
**[[Virtual Environment|Environment]]<br />
**[[Punishments in Virtual Environments|Punishment]]<br />
**[[Virtual Rape|Rape]]<br />
**''Sex in'' [[Cybersex|Cyberspace]], [[Online Dating#Sex|Online Dating]]<br />
**''Stalking in'' [[Cyberstalking|Cyberspace]]<br />
*'''''[[Virtual Reality]]'''''<br />
**[[Virtual Reality and Computer Simulations|Computer Simulations]]<br />
**[[Virtual Reality in Online Role Playing Games|Online Role Playing Games]]<br />
*[[Information Assurance/Threats|Virus (Malware)]]<br />
*[[Vuze]]<br />
<br />
=== W ===<br />
----<br />
*[[Warcraft III]]<br />
*[[Wattpad]]<br />
*[[Waze]]<br />
*[[Web 2.0]]<br />
*[[Weibo]]<br />
*[[Whisper]]<br />
*[[Wii U]]<br />
*[[Wiki]]<br />
*[[WikiLeaks]]<br />
*[[Wikipedia]]<br />
*[[W.J.T. Mitchell]]<br />
*[[Women in Gaming]]<br />
*[[World of Warcraft]]<br />
*[[Information Assurance/Threats|Worm (Malware)]]<br />
<br />
=== X ===<br />
----<br />
*[[The X-Files]]<br />
*[[Xkcd]]<br />
<br />
<br />
=== Y ===<br />
----<br />
*[[Yik Yak]]<br />
*''YouTube''<br />
**[[YouTube|YouTube (Website)]]<br />
**[[YouTube Beauty Community|Beauty Community]]<br />
<br />
=== Z ===<br />
----<br />
*[[Zattoo]]<br />
*[[Zynga]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Ransomware&diff=57019Ransomware2016-04-24T18:03:39Z<p>Zzasuwa: /* See Also */</p>
<hr />
<div>[[File:Ransomware_2.png|right|thumb|350px]]<br />
'''Ransomware''' is ...<br />
<br />
==Heading #1==<br />
<br />
===Subheading #1===<br />
<br />
==Heading #2==<br />
<br />
===Subheading #2===<br />
<br />
==Ethical Implications==<br />
<br />
===Ethics Subheading===<br />
<br />
==See Also==<br />
* [[Information_Assurance/Threats|Information Assurance/Threats]]<br />
* [[Spam]]<br />
* [[Cyberwarfare]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Virtual Environments, Concerns, & Issues]]<br />
[[category: Information Ethics]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Ransomware&diff=57018Ransomware2016-04-24T18:02:32Z<p>Zzasuwa: Created Ransomware page</p>
<hr />
<div>[[File:Ransomware_2.png|right|thumb|350px]]<br />
'''Ransomware''' is ...<br />
<br />
==Heading #1==<br />
<br />
===Subheading #1===<br />
<br />
==Heading #2==<br />
<br />
===Subheading #2===<br />
<br />
==Ethical Implications==<br />
<br />
===Ethics Subheading===<br />
<br />
==See Also==<br />
* [[Information_Assurance/Threats]]<br />
* [[Spam]]<br />
* [[Cyberwarfare]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Virtual Environments, Concerns, & Issues]]<br />
[[category: Information Ethics]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=File:Ransomware_2.png&diff=57016File:Ransomware 2.png2016-04-24T17:49:12Z<p>Zzasuwa: </p>
<hr />
<div></div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=File:Ransomware_1.jpg&diff=57015File:Ransomware 1.jpg2016-04-24T17:48:56Z<p>Zzasuwa: </p>
<hr />
<div></div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Information_Security&diff=56983Information Security2016-04-23T04:31:43Z<p>Zzasuwa: /* Ethics and Laws */</p>
<hr />
<div>'''Information Security''' is the process of protecting information from unintended access by others. The methodologies for protecting information varies based on the type of information being protected, to whom the information currently belongs, and how the information could potentially be utilized by others. <br />
<br />
Concern with the security of information has become more concentrated with the proliferation of electronic information storage mechanisms, and subsequently with the spread of information in an online environment. <ref>National Institute of Standards and Technology: Information Security http://csrc.nist.gov/publications/nistpubs/800-64-Rev2/SP800-64-Revision2.pdf </ref> These mediums of information transportation have both helped and hindered the process of data protection. For instance, by allowing information to be encrypted and decrypted in a complex manner when being transferred from one point to another, data can be protected in a more robust way. Conversely, the ease with which information can be copied and disseminated without expressed consent of the information-holder can cause it to be used in nefarious ways.<br />
<br />
[[File:Information-security-image.png|300px|thumb|right|Protecting bits and bytes can have as much of a real-world impact as protecting physical objects.]]<br />
<br />
== Conceptual Overview ==<br />
Protecting private information is important to ensure that information is both reliable and confidential. When information is not protected in most formats, it can be tampered with causing inaccuracies or discrepancies. If the information is valuable and is not protected, it can be distributed to parties that could cause harm to it in some way. The CIA Model of Information Security (Confidentiality-Integrity-Availability) <ref>Information Systems Security Association: CIA Information Security Model http://www.issa.org/images/upload/files/Parker-Simplistic%20Information%20Security%20Model.pdf</ref> is a fundamental way of describing the steps necessary for protecting information.<br />
<br />
[[File:CIA-triad.png|300px|thumb|right|The CIA Model of Information Security consists of three components for correctly protecting information.]]<br />
<br />
=== Information Confidentiality ===<br />
The process of ensuring that information is available only to those who are authorized to view it. Disclosure of parts, or the entirety of sensitive information can harm those to whom the information belongs, as well as the inherent value of the information itself. Authentication methods IDs, passwords, pons, etc, reinforces what confidentiality is good for.<br />
<br />
=== Information Integrity ===<br />
Also called information reliability, it is of the utmost importance that information is accurate, up-to-date, and complete for those who plan to use it. Protecting information against unwanted modification or destruction is a significant part of securing information.<br />
<br />
=== Information Availability === <br />
Providing access to protected information in both a timely, reliable manner helps those who are monitoring it and using it to discover issues or changes in the information itself.<br />
<br />
===Information Security and Privacy===<br />
As an important subtopic or derivative motivating force behind information security, information privacy compels many companies, governments, and people alike to consider the implications of lax security measures. Regardless of the context or scale, the degree to which information is accessible to a given audience is directly representative of its vulnerability and by extension, its inherent security (or that of its external system) and ability to defend against intrusions. Bolstering superior defensive measures not only amounts to better technology, but can contribute to improved customer satisfaction, less server down time, and the opportunity to enhance a network’s interoperability without fear of unwarranted exploitation. Striking the right balance between the open and closed nature of an information system is equally critical for common business concerns as it is for the concurrent necessity of keeping its security in the best condition possible.<br />
<br />
== Information Security and Electronic Storage ==<br />
The advent of data-transfer via electronic means on the internet has shifted the focus of information security from physical protection (protecting the actual medium the information is stored on) to a more broad definition of what protection means. Prior to computerization of data, often the easiest way to protect information was to reduce access to the physical medium on which the information was kept. This could be done by managing who could access the stored mediums where the information was kept (ie. determining who could access a filing cabinet with important paperwork). The low-cost of information replication in an electronic format, and the difficulty of identifying who is viewing information has greatly changed the ways in which information needs to be protected. <br />
<br />
=== Access Controls ===<br />
The foremost step in identifying who a potential information user is before allowing them to view or manipulating data in an electronic environment. <ref>Handbook of Information Security: Access Controls http://www.cccure.org/Documents/HISM/001-002.html</ref> Creating profiles of a user's identity can be a first step in allowing them access to sensitive information. These profiles can then be protected with unique passwords that allow data-protection systems to authenticate their identity before allowing them to access information. An individual's behavior while using information can also be monitored by connecting their actions to a unique profile.<br />
<br />
=== Data Encryption ===<br />
Information can be protected when it is being transferred from point-to-point by using processes to encrypt, or jumble, the data while in transit, and then re-assemble it upon arrival at its destination. Also called cryptography, the process of encrypting and decrypting data between two points using a shared key is a way of providing information security. <ref>[http://www.sciencedirect.com.proxy.lib.umich.edu/science?_ob=MiamiImageURL&_cid=271887&_user=99318&_pii=S0167404897822432&_check=y&_origin=&_coverDate=31-Dec-1997&view=c&wchp=dGLzVlS-zSkzS&md5=97fca3706a03ffaf56cf260a03cff957/1-s2.0-S0167404897822432-main.pdf] Harold Joseph, H. (1997). Data encryption: A non-mathematical approach. Computers & Security, 16(5), 369-386. doi:10.1016/S0167-4048(97)82243-2<br />
</ref><br />
<br />
==Ethics of Information Security==<br />
Determining social expectations for protecting information is a societal-wide undertaking. Without a common notion of what protecting information entails, an individual's personal data can easily face unnecessarily jeopardizing circumstances. Generally, protecting important personal information is a necessity defined by society at large. As a result of these common beliefs surrounding information security, the current practice in most Western societies is that companies and individuals must jointly undertake the responsibility of protecting an individual's personal information in order to prevent it from being misused.<br />
<br />
It is also important to note that protecting information is not merely carried out on a one-time basis when data is created or stored; ideally, it is an iterative process that takes places throughout an information object’s entire lifetime. As the shape and composition of an information article is subject to change over time, the methods by which it is protected are also liable to the same type of evolutionary change.<br />
<br />
=== Individual Information Privacy ===<br />
The security of an individual's personal information is inextricably tied to their personal privacy. When an individual interacts with other parties using their private information, especially in the online environment, it is hard to guarantee that this information will retain its original integrity. Companies have a legal obligation within the United States to provide the protection of their customers’ personal information during a business transaction, especially when conducted in an online environment.<ref>U.S. Governmental Printing Office: Electronic Code of Federal Regulations http://ecfr.gpoaccess.gov/cgi/t/text/text-idx?c=ecfr&sid=a273032f4545305b53bd3b788739f586&tpl=/ecfrbrowse/Title16/16cfr681_main_02.tpl</ref><br />
<br />
===Ethics and Laws===<br />
Developing all-encompassing laws to cover every facet of an information society -- up to and including disciplinary measures for security infringements -- is an unreasonable expectation for the breadth of unique scenarios is an untenable obstacle. Because of the natural disparity in beliefs and thus moral principles between individuals, arriving at a universal understanding is doubtful and if attempted, insufficiently robust for continuous societal growth.<ref>Dell SecureWorks. (2011, February 7). Crossing the Line: Ethics for the Security Professional. Retrieved April 23, 2016, from https://www.secureworks.com/blog/ethics</ref> Whereas standing ethical tenets are objective from the perspective of their construction and operable range, the way they are interpreted is widely left to subjective interpretation.<ref>Philip, A. R. (2002). The Legal System and Ethics in Information Security. SANS Institute. (PDF). Retrieved April 23, 2016, from https://www.sans.org/reading-room/whitepapers/legal/legal-system-ethics-information-security-54</ref> Therefore, it is largely the responsibility of members of society to establish positive standards of ethical behavior and promote them as best they can for maximal diffusion. In this respect, ethics and the rule of law work hand-in-hand to ensure that individuals’ privacy is protected and that society thrives despite the challenges presented by information security. Essential to this effort is the collaboration between people, their governments, and the relationships that exist among them.<ref>[https://www.sans.org/reading-room/whitepapers/legal/legal-system-ethics-information-security-54] Retrieved on 4-23-2016</ref> Striving for a culture rooted in some form of “ethical abidance” is paramount for diverging from our long trusted dependency on laws for enforcing disobedience.<ref>[https://www.sans.org/reading-room/whitepapers/legal/legal-system-ethics-information-security-54] Retrieved on 4-23-2016</ref> As norm-driven behavior is often more reliably pursued with a motivation based on passion than mandatory sentencing (i.e. laws), its comparable efficacy for modifying behavior on a large scale (needed for encouraging widespread adoption of digital and informationally robust security ethics), far outweighs the potential for laws and regulatory bodies to do the same.<br />
<br />
== See Also ==<br />
* [[Cyber Law]]<br />
* [[Information Ethics]]<br />
* [[Information Transparency]]<br />
* [[Online Identity]]<br />
* [[Online Identity Theft]]<br />
* [[Virtual Environment]]<br />
<br />
== References ==<br />
<references/><br />
[[Category:piracy]]<br />
<br />
([[Topics|back to index]])</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Information_Security&diff=56982Information Security2016-04-23T04:18:01Z<p>Zzasuwa: /* Ethics of Information Security */</p>
<hr />
<div>'''Information Security''' is the process of protecting information from unintended access by others. The methodologies for protecting information varies based on the type of information being protected, to whom the information currently belongs, and how the information could potentially be utilized by others. <br />
<br />
Concern with the security of information has become more concentrated with the proliferation of electronic information storage mechanisms, and subsequently with the spread of information in an online environment. <ref>National Institute of Standards and Technology: Information Security http://csrc.nist.gov/publications/nistpubs/800-64-Rev2/SP800-64-Revision2.pdf </ref> These mediums of information transportation have both helped and hindered the process of data protection. For instance, by allowing information to be encrypted and decrypted in a complex manner when being transferred from one point to another, data can be protected in a more robust way. Conversely, the ease with which information can be copied and disseminated without expressed consent of the information-holder can cause it to be used in nefarious ways.<br />
<br />
[[File:Information-security-image.png|300px|thumb|right|Protecting bits and bytes can have as much of a real-world impact as protecting physical objects.]]<br />
<br />
== Conceptual Overview ==<br />
Protecting private information is important to ensure that information is both reliable and confidential. When information is not protected in most formats, it can be tampered with causing inaccuracies or discrepancies. If the information is valuable and is not protected, it can be distributed to parties that could cause harm to it in some way. The CIA Model of Information Security (Confidentiality-Integrity-Availability) <ref>Information Systems Security Association: CIA Information Security Model http://www.issa.org/images/upload/files/Parker-Simplistic%20Information%20Security%20Model.pdf</ref> is a fundamental way of describing the steps necessary for protecting information.<br />
<br />
[[File:CIA-triad.png|300px|thumb|right|The CIA Model of Information Security consists of three components for correctly protecting information.]]<br />
<br />
=== Information Confidentiality ===<br />
The process of ensuring that information is available only to those who are authorized to view it. Disclosure of parts, or the entirety of sensitive information can harm those to whom the information belongs, as well as the inherent value of the information itself. Authentication methods IDs, passwords, pons, etc, reinforces what confidentiality is good for.<br />
<br />
=== Information Integrity ===<br />
Also called information reliability, it is of the utmost importance that information is accurate, up-to-date, and complete for those who plan to use it. Protecting information against unwanted modification or destruction is a significant part of securing information.<br />
<br />
=== Information Availability === <br />
Providing access to protected information in both a timely, reliable manner helps those who are monitoring it and using it to discover issues or changes in the information itself.<br />
<br />
===Information Security and Privacy===<br />
As an important subtopic or derivative motivating force behind information security, information privacy compels many companies, governments, and people alike to consider the implications of lax security measures. Regardless of the context or scale, the degree to which information is accessible to a given audience is directly representative of its vulnerability and by extension, its inherent security (or that of its external system) and ability to defend against intrusions. Bolstering superior defensive measures not only amounts to better technology, but can contribute to improved customer satisfaction, less server down time, and the opportunity to enhance a network’s interoperability without fear of unwarranted exploitation. Striking the right balance between the open and closed nature of an information system is equally critical for common business concerns as it is for the concurrent necessity of keeping its security in the best condition possible.<br />
<br />
== Information Security and Electronic Storage ==<br />
The advent of data-transfer via electronic means on the internet has shifted the focus of information security from physical protection (protecting the actual medium the information is stored on) to a more broad definition of what protection means. Prior to computerization of data, often the easiest way to protect information was to reduce access to the physical medium on which the information was kept. This could be done by managing who could access the stored mediums where the information was kept (ie. determining who could access a filing cabinet with important paperwork). The low-cost of information replication in an electronic format, and the difficulty of identifying who is viewing information has greatly changed the ways in which information needs to be protected. <br />
<br />
=== Access Controls ===<br />
The foremost step in identifying who a potential information user is before allowing them to view or manipulating data in an electronic environment. <ref>Handbook of Information Security: Access Controls http://www.cccure.org/Documents/HISM/001-002.html</ref> Creating profiles of a user's identity can be a first step in allowing them access to sensitive information. These profiles can then be protected with unique passwords that allow data-protection systems to authenticate their identity before allowing them to access information. An individual's behavior while using information can also be monitored by connecting their actions to a unique profile.<br />
<br />
=== Data Encryption ===<br />
Information can be protected when it is being transferred from point-to-point by using processes to encrypt, or jumble, the data while in transit, and then re-assemble it upon arrival at its destination. Also called cryptography, the process of encrypting and decrypting data between two points using a shared key is a way of providing information security. <ref>[http://www.sciencedirect.com.proxy.lib.umich.edu/science?_ob=MiamiImageURL&_cid=271887&_user=99318&_pii=S0167404897822432&_check=y&_origin=&_coverDate=31-Dec-1997&view=c&wchp=dGLzVlS-zSkzS&md5=97fca3706a03ffaf56cf260a03cff957/1-s2.0-S0167404897822432-main.pdf] Harold Joseph, H. (1997). Data encryption: A non-mathematical approach. Computers & Security, 16(5), 369-386. doi:10.1016/S0167-4048(97)82243-2<br />
</ref><br />
<br />
==Ethics of Information Security==<br />
Determining social expectations for protecting information is a societal-wide undertaking. Without a common notion of what protecting information entails, an individual's personal data can easily face unnecessarily jeopardizing circumstances. Generally, protecting important personal information is a necessity defined by society at large. As a result of these common beliefs surrounding information security, the current practice in most Western societies is that companies and individuals must jointly undertake the responsibility of protecting an individual's personal information in order to prevent it from being misused.<br />
<br />
It is also important to note that protecting information is not merely carried out on a one-time basis when data is created or stored; ideally, it is an iterative process that takes places throughout an information object’s entire lifetime. As the shape and composition of an information article is subject to change over time, the methods by which it is protected are also liable to the same type of evolutionary change.<br />
<br />
=== Individual Information Privacy ===<br />
The security of an individual's personal information is inextricably tied to their personal privacy. When an individual interacts with other parties using their private information, especially in the online environment, it is hard to guarantee that this information will retain its original integrity. Companies have a legal obligation within the United States to provide the protection of their customers’ personal information during a business transaction, especially when conducted in an online environment.<ref>U.S. Governmental Printing Office: Electronic Code of Federal Regulations http://ecfr.gpoaccess.gov/cgi/t/text/text-idx?c=ecfr&sid=a273032f4545305b53bd3b788739f586&tpl=/ecfrbrowse/Title16/16cfr681_main_02.tpl</ref><br />
<br />
===Ethics and Laws===<br />
Developing all-encompassing laws to cover every facet of an information society -- up to and including disciplinary measures for security infringements -- is an unreasonable expectation for the breadth of unique scenarios is an untenable obstacle. Because of the natural disparity in beliefs and thus moral principles between individuals, arriving at a universal understanding is doubtful and insufficiently robust.<ref>Dell SecureWorks. (2011, February 7). Crossing the Line: Ethics for the Security Professional. Retrieved April 23, 2016, from https://www.secureworks.com/blog/ethics</ref> Whereas standing ethical tenets are objective from the perspective of their construction and operable range, the way they are interpreted is widely left to subjective interpretation.<ref>Philip, A. R. (2002). The Legal System and Ethics in Information Security. SANS Institute. (PDF). Retrieved April 23, 2016, from https://www.sans.org/reading-room/whitepapers/legal/legal-system-ethics-information-security-54</ref> Therefore, it is largely the responsibility of members of society to establish positive standards of ethical behavior and promote them as best they can for maximal diffusion. In this respect, ethics and the rule of law work hand-in-hand to ensure that individuals’ privacy is protected and that society thrives despite the challenges presented by information security. Essential to this effort is the collaboration between people, their governments, and the relationships that exist among them.<ref>[https://www.sans.org/reading-room/whitepapers/legal/legal-system-ethics-information-security-54] Retrieved on 4-23-2016</ref> Striving for a culture rooted in some form of “ethical abidance” is paramount for diverging from our long trusted dependency on laws for enforcing disobedience.<ref>[https://www.sans.org/reading-room/whitepapers/legal/legal-system-ethics-information-security-54] Retrieved on 4-23-2016</ref> As norm-driven behavior is often more reliably pursued with a motivation based on passion than mandatory sentencing (i.e. laws), its comparable efficacy for modifying behavior on a large scale (needed for encouraging widespread adoption of digital and informationally robust security ethics), far outweighs the potential for laws and regulatory bodies to do the same.<br />
<br />
== See Also ==<br />
* [[Cyber Law]]<br />
* [[Information Ethics]]<br />
* [[Information Transparency]]<br />
* [[Online Identity]]<br />
* [[Online Identity Theft]]<br />
* [[Virtual Environment]]<br />
<br />
== References ==<br />
<references/><br />
[[Category:piracy]]<br />
<br />
([[Topics|back to index]])</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Information_Security&diff=56981Information Security2016-04-23T01:49:55Z<p>Zzasuwa: Modified section title</p>
<hr />
<div>'''Information Security''' is the process of protecting information from unintended access by others. The methodologies for protecting information varies based on the type of information being protected, to whom the information currently belongs, and how the information could potentially be utilized by others. <br />
<br />
Concern with the security of information has become more concentrated with the proliferation of electronic information storage mechanisms, and subsequently with the spread of information in an online environment. <ref>National Institute of Standards and Technology: Information Security http://csrc.nist.gov/publications/nistpubs/800-64-Rev2/SP800-64-Revision2.pdf </ref> These mediums of information transportation have both helped and hindered the process of data protection. For instance, by allowing information to be encrypted and decrypted in a complex manner when being transferred from one point to another, data can be protected in a more robust way. Conversely, the ease with which information can be copied and disseminated without expressed consent of the information-holder can cause it to be used in nefarious ways.<br />
<br />
[[File:Information-security-image.png|300px|thumb|right|Protecting bits and bytes can have as much of a real-world impact as protecting physical objects.]]<br />
<br />
== Conceptual Overview ==<br />
Protecting private information is important to ensure that information is both reliable and confidential. When information is not protected in most formats, it can be tampered with causing inaccuracies or discrepancies. If the information is valuable and is not protected, it can be distributed to parties that could cause harm to it in some way. The CIA Model of Information Security (Confidentiality-Integrity-Availability) <ref>Information Systems Security Association: CIA Information Security Model http://www.issa.org/images/upload/files/Parker-Simplistic%20Information%20Security%20Model.pdf</ref> is a fundamental way of describing the steps necessary for protecting information.<br />
<br />
[[File:CIA-triad.png|300px|thumb|right|The CIA Model of Information Security consists of three components for correctly protecting information.]]<br />
<br />
=== Information Confidentiality ===<br />
The process of ensuring that information is available only to those who are authorized to view it. Disclosure of parts, or the entirety of sensitive information can harm those to whom the information belongs, as well as the inherent value of the information itself. Authentication methods IDs, passwords, pons, etc, reinforces what confidentiality is good for.<br />
<br />
=== Information Integrity ===<br />
Also called information reliability, it is of the utmost importance that information is accurate, up-to-date, and complete for those who plan to use it. Protecting information against unwanted modification or destruction is a significant part of securing information.<br />
<br />
=== Information Availability === <br />
Providing access to protected information in both a timely, reliable manner helps those who are monitoring it and using it to discover issues or changes in the information itself.<br />
<br />
===Information Security and Privacy===<br />
As an important subtopic or derivative motivating force behind information security, information privacy compels many companies, governments, and people alike to consider the implications of lax security measures. Regardless of the context or scale, the degree to which information is accessible to a given audience is directly representative of its vulnerability and by extension, its inherent security (or that of its external system) and ability to defend against intrusions. Bolstering superior defensive measures not only amounts to better technology, but can contribute to improved customer satisfaction, less server down time, and the opportunity to enhance a network’s interoperability without fear of unwarranted exploitation. Striking the right balance between the open and closed nature of an information system is equally critical for common business concerns as it is for the concurrent necessity of keeping its security in the best condition possible.<br />
<br />
== Information Security and Electronic Storage ==<br />
The advent of data-transfer via electronic means on the internet has shifted the focus of information security from physical protection (protecting the actual medium the information is stored on) to a more broad definition of what protection means. Prior to computerization of data, often the easiest way to protect information was to reduce access to the physical medium on which the information was kept. This could be done by managing who could access the stored mediums where the information was kept (ie. determining who could access a filing cabinet with important paperwork). The low-cost of information replication in an electronic format, and the difficulty of identifying who is viewing information has greatly changed the ways in which information needs to be protected. <br />
<br />
=== Access Controls ===<br />
The foremost step in identifying who a potential information user is before allowing them to view or manipulating data in an electronic environment. <ref>Handbook of Information Security: Access Controls http://www.cccure.org/Documents/HISM/001-002.html</ref> Creating profiles of a user's identity can be a first step in allowing them access to sensitive information. These profiles can then be protected with unique passwords that allow data-protection systems to authenticate their identity before allowing them to access information. An individual's behavior while using information can also be monitored by connecting their actions to a unique profile.<br />
<br />
=== Data Encryption ===<br />
Information can be protected when it is being transferred from point-to-point by using processes to encrypt, or jumble, the data while in transit, and then re-assemble it upon arrival at its destination. Also called cryptography, the process of encrypting and decrypting data between two points using a shared key is a way of providing information security. <ref>[http://www.sciencedirect.com.proxy.lib.umich.edu/science?_ob=MiamiImageURL&_cid=271887&_user=99318&_pii=S0167404897822432&_check=y&_origin=&_coverDate=31-Dec-1997&view=c&wchp=dGLzVlS-zSkzS&md5=97fca3706a03ffaf56cf260a03cff957/1-s2.0-S0167404897822432-main.pdf] Harold Joseph, H. (1997). Data encryption: A non-mathematical approach. Computers & Security, 16(5), 369-386. doi:10.1016/S0167-4048(97)82243-2<br />
</ref><br />
<br />
==Ethics of Information Security==<br />
Determining social expectations for protecting information is a societal-wide undertaking. Without a common notion of what protecting information entails, an individual's personal data can easily face unnecessarily jeopardizing circumstances. Generally, protecting important personal information is a necessity defined by society at large. As a result of these common beliefs surrounding information security, the current practice in most Western societies is that companies and individuals must jointly undertake the responsibility of protecting an individual's personal information in order to prevent it from being misused.<br />
<br />
It is also important to note that protecting information is not merely carried out on a one-time basis when data is created or stored; ideally, it is an iterative process that takes places throughout an information object’s entire lifetime. As the shape and composition of an information article is subject to change over time, the methods by which it is protected are also liable to the same type of evolutionary change.<br />
<br />
=== Individual Information Privacy ===<br />
The security of an individual's personal information is inextricably tied to their personal privacy. When an individual interacts with other parties using their private information, especially in the online environment, it is hard to guarantee that this information will retain its original integrity. Companies have a legal obligation within the United States to provide the protection of their customers’ personal information during a business transaction, especially when conducted in an online environment.<ref>U.S. Governmental Printing Office: Electronic Code of Federal Regulations http://ecfr.gpoaccess.gov/cgi/t/text/text-idx?c=ecfr&sid=a273032f4545305b53bd3b788739f586&tpl=/ecfrbrowse/Title16/16cfr681_main_02.tpl</ref><br />
<br />
== See Also ==<br />
* [[Cyber Law]]<br />
* [[Information Ethics]]<br />
* [[Information Transparency]]<br />
* [[Online Identity]]<br />
* [[Online Identity Theft]]<br />
* [[Virtual Environment]]<br />
<br />
== References ==<br />
<references/><br />
[[Category:piracy]]<br />
<br />
([[Topics|back to index]])</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Information_Security&diff=56980Information Security2016-04-23T01:43:14Z<p>Zzasuwa: /* Conceptual Overview */</p>
<hr />
<div>'''Information Security''' is the process of protecting information from unintended access by others. The methodologies for protecting information varies based on the type of information being protected, to whom the information currently belongs, and how the information could potentially be utilized by others. <br />
<br />
Concern with the security of information has become more concentrated with the proliferation of electronic information storage mechanisms, and subsequently with the spread of information in an online environment. <ref>National Institute of Standards and Technology: Information Security http://csrc.nist.gov/publications/nistpubs/800-64-Rev2/SP800-64-Revision2.pdf </ref> These mediums of information transportation have both helped and hindered the process of data protection. For instance, by allowing information to be encrypted and decrypted in a complex manner when being transferred from one point to another, data can be protected in a more robust way. Conversely, the ease with which information can be copied and disseminated without expressed consent of the information-holder can cause it to be used in nefarious ways.<br />
<br />
[[File:Information-security-image.png|300px|thumb|right|Protecting bits and bytes can have as much of a real-world impact as protecting physical objects.]]<br />
<br />
== Conceptual Overview ==<br />
Protecting private information is important to ensure that information is both reliable and confidential. When information is not protected in most formats, it can be tampered with causing inaccuracies or discrepancies. If the information is valuable and is not protected, it can be distributed to parties that could cause harm to it in some way. The CIA Model of Information Security (Confidentiality-Integrity-Availability) <ref>Information Systems Security Association: CIA Information Security Model http://www.issa.org/images/upload/files/Parker-Simplistic%20Information%20Security%20Model.pdf</ref> is a fundamental way of describing the steps necessary for protecting information.<br />
<br />
[[File:CIA-triad.png|300px|thumb|right|The CIA Model of Information Security consists of three components for correctly protecting information.]]<br />
<br />
=== Information Confidentiality ===<br />
The process of ensuring that information is available only to those who are authorized to view it. Disclosure of parts, or the entirety of sensitive information can harm those to whom the information belongs, as well as the inherent value of the information itself. Authentication methods IDs, passwords, pons, etc, reinforces what confidentiality is good for.<br />
<br />
=== Information Integrity ===<br />
Also called information reliability, it is of the utmost importance that information is accurate, up-to-date, and complete for those who plan to use it. Protecting information against unwanted modification or destruction is a significant part of securing information.<br />
<br />
=== Information Availability === <br />
Providing access to protected information in both a timely, reliable manner helps those who are monitoring it and using it to discover issues or changes in the information itself.<br />
<br />
===Information Security and Privacy===<br />
As an important subtopic or derivative motivating force behind information security, information privacy compels many companies, governments, and people alike to consider the implications of lax security measures. Regardless of the context or scale, the degree to which information is accessible to a given audience is directly representative of its vulnerability and by extension, its inherent security (or that of its external system) and ability to defend against intrusions. Bolstering superior defensive measures not only amounts to better technology, but can contribute to improved customer satisfaction, less server down time, and the opportunity to enhance a network’s interoperability without fear of unwarranted exploitation. Striking the right balance between the open and closed nature of an information system is equally critical for common business concerns as it is for the concurrent necessity of keeping its security in the best condition possible.<br />
<br />
== Information Security and Electronic Storage ==<br />
The advent of data-transfer via electronic means on the internet has shifted the focus of information security from physical protection (protecting the actual medium the information is stored on) to a more broad definition of what protection means. Prior to computerization of data, often the easiest way to protect information was to reduce access to the physical medium on which the information was kept. This could be done by managing who could access the stored mediums where the information was kept (ie. determining who could access a filing cabinet with important paperwork). The low-cost of information replication in an electronic format, and the difficulty of identifying who is viewing information has greatly changed the ways in which information needs to be protected. <br />
<br />
=== Access Controls ===<br />
The foremost step in identifying who a potential information user is before allowing them to view or manipulating data in an electronic environment. <ref>Handbook of Information Security: Access Controls http://www.cccure.org/Documents/HISM/001-002.html</ref> Creating profiles of a user's identity can be a first step in allowing them access to sensitive information. These profiles can then be protected with unique passwords that allow data-protection systems to authenticate their identity before allowing them to access information. An individual's behavior while using information can also be monitored by connecting their actions to a unique profile.<br />
<br />
=== Data Encryption ===<br />
Information can be protected when it is being transferred from point-to-point by using processes to encrypt, or jumble, the data while in transit, and then re-assemble it upon arrival at its destination. Also called cryptography, the process of encrypting and decrypting data between two points using a shared key is a way of providing information security. <ref>[http://www.sciencedirect.com.proxy.lib.umich.edu/science?_ob=MiamiImageURL&_cid=271887&_user=99318&_pii=S0167404897822432&_check=y&_origin=&_coverDate=31-Dec-1997&view=c&wchp=dGLzVlS-zSkzS&md5=97fca3706a03ffaf56cf260a03cff957/1-s2.0-S0167404897822432-main.pdf] Harold Joseph, H. (1997). Data encryption: A non-mathematical approach. Computers & Security, 16(5), 369-386. doi:10.1016/S0167-4048(97)82243-2<br />
</ref><br />
<br />
== Ethics of Information Privacy (Under Construction, 4/22/2016) ==<br />
Determining social expectations for protecting information is a societal-wide undertaking. Without a common notion of what protecting information entails, an individual's personal data can easily face unnecessarily jeopardizing circumstances. Generally, protecting important personal information is a necessity defined by society at large. As a result of these common beliefs surrounding information security, the current practice in most Western societies is that companies and individuals must jointly undertake the responsibility of protecting an individual's personal information in order to prevent it from being misused.<br />
<br />
It is also important to note that protecting information is not merely carried out on a one-time basis when data is created or stored; ideally, it is an iterative process that takes places throughout an information object’s entire lifetime. As the shape and composition of an information article is subject to change over time, the methods by which it is protected are also liable to the same type of evolutionary change.<br />
<br />
=== Individual Information Privacy ===<br />
The security of an individual's personal information is inextricably tied to their personal privacy. When an individual interacts with other parties using their private information, especially in the online environment, it is hard to guarantee that this information will retain its original integrity. Companies have a legal obligation within the United States to provide the protection of their customers’ personal information during a business transaction, especially when conducted in an online environment.<ref>U.S. Governmental Printing Office: Electronic Code of Federal Regulations http://ecfr.gpoaccess.gov/cgi/t/text/text-idx?c=ecfr&sid=a273032f4545305b53bd3b788739f586&tpl=/ecfrbrowse/Title16/16cfr681_main_02.tpl</ref><br />
<br />
== See Also ==<br />
* [[Cyber Law]]<br />
* [[Information Ethics]]<br />
* [[Information Transparency]]<br />
* [[Online Identity]]<br />
* [[Online Identity Theft]]<br />
* [[Virtual Environment]]<br />
<br />
== References ==<br />
<references/><br />
[[Category:piracy]]<br />
<br />
([[Topics|back to index]])</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Information_Security&diff=56979Information Security2016-04-23T00:56:33Z<p>Zzasuwa: Corrected existing wording/flow</p>
<hr />
<div>'''Information Security''' is the process of protecting information from unintended access by others. The methodologies for protecting information varies based on the type of information being protected, to whom the information currently belongs, and how the information could potentially be utilized by others. <br />
<br />
Concern with the security of information has become more concentrated with the proliferation of electronic information storage mechanisms, and subsequently with the spread of information in an online environment. <ref>National Institute of Standards and Technology: Information Security http://csrc.nist.gov/publications/nistpubs/800-64-Rev2/SP800-64-Revision2.pdf </ref> These mediums of information transportation have both helped and hindered the process of data protection. For instance, by allowing information to be encrypted and decrypted in a complex manner when being transferred from one point to another, data can be protected in a more robust way. Conversely, the ease with which information can be copied and disseminated without expressed consent of the information-holder can cause it to be used in nefarious ways.<br />
<br />
[[File:Information-security-image.png|300px|thumb|right|Protecting bits and bytes can have as much of a real-world impact as protecting physical objects.]]<br />
<br />
== Conceptual Overview ==<br />
Protecting private information is important to ensure that information is both reliable and confidential. When information is not protected in most formats, it can be tampered with causing inaccuracies or discrepancies. If the information is valuable and is not protected, it can be distributed to parties that could cause harm to it in some way. The CIA Model of Information Security (Confidentiality-Integrity-Availability) <ref>Information Systems Security Association: CIA Information Security Model http://www.issa.org/images/upload/files/Parker-Simplistic%20Information%20Security%20Model.pdf</ref> is a fundamental way of describing the steps necessary for protecting information.<br />
<br />
[[File:CIA-triad.png|300px|thumb|right|The CIA Model of Information Security consists of three components for correctly protecting information.]]<br />
<br />
=== Information Confidentiality ===<br />
The process of ensuring that information is available only to those who are authorized to view it. Disclosure of parts, or the entirety of sensitive information can harm those to whom the information belongs, as well as the inherent value of the information itself. Authentication methods IDs, passwords, pons, etc, reinforces what confidentiality is good for.<br />
<br />
=== Information Integrity ===<br />
Also called information reliability, it is of the utmost importance that information is accurate, up-to-date, and complete for those who plan to use it. Protecting information against unwanted modification or destruction is a significant part of securing information.<br />
<br />
=== Information Availability === <br />
Providing access to protected information in both a timely, reliable manner helps those who are monitoring it and using it to discover issues or changes in the information itself.<br />
<br />
== Information Security and Electronic Storage ==<br />
The advent of data-transfer via electronic means on the internet has shifted the focus of information security from physical protection (protecting the actual medium the information is stored on) to a more broad definition of what protection means. Prior to computerization of data, often the easiest way to protect information was to reduce access to the physical medium on which the information was kept. This could be done by managing who could access the stored mediums where the information was kept (ie. determining who could access a filing cabinet with important paperwork). The low-cost of information replication in an electronic format, and the difficulty of identifying who is viewing information has greatly changed the ways in which information needs to be protected. <br />
<br />
=== Access Controls ===<br />
The foremost step in identifying who a potential information user is before allowing them to view or manipulating data in an electronic environment. <ref>Handbook of Information Security: Access Controls http://www.cccure.org/Documents/HISM/001-002.html</ref> Creating profiles of a user's identity can be a first step in allowing them access to sensitive information. These profiles can then be protected with unique passwords that allow data-protection systems to authenticate their identity before allowing them to access information. An individual's behavior while using information can also be monitored by connecting their actions to a unique profile.<br />
<br />
=== Data Encryption ===<br />
Information can be protected when it is being transferred from point-to-point by using processes to encrypt, or jumble, the data while in transit, and then re-assemble it upon arrival at its destination. Also called cryptography, the process of encrypting and decrypting data between two points using a shared key is a way of providing information security. <ref>[http://www.sciencedirect.com.proxy.lib.umich.edu/science?_ob=MiamiImageURL&_cid=271887&_user=99318&_pii=S0167404897822432&_check=y&_origin=&_coverDate=31-Dec-1997&view=c&wchp=dGLzVlS-zSkzS&md5=97fca3706a03ffaf56cf260a03cff957/1-s2.0-S0167404897822432-main.pdf] Harold Joseph, H. (1997). Data encryption: A non-mathematical approach. Computers & Security, 16(5), 369-386. doi:10.1016/S0167-4048(97)82243-2<br />
</ref><br />
<br />
== Ethics of Information Privacy (Under Construction, 4/22/2016) ==<br />
Determining social expectations for protecting information is a societal-wide undertaking. Without a common notion of what protecting information entails, an individual's personal data can easily face unnecessarily jeopardizing circumstances. Generally, protecting important personal information is a necessity defined by society at large. As a result of these common beliefs surrounding information security, the current practice in most Western societies is that companies and individuals must jointly undertake the responsibility of protecting an individual's personal information in order to prevent it from being misused.<br />
<br />
It is also important to note that protecting information is not merely carried out on a one-time basis when data is created or stored; ideally, it is an iterative process that takes places throughout an information object’s entire lifetime. As the shape and composition of an information article is subject to change over time, the methods by which it is protected are also liable to the same type of evolutionary change.<br />
<br />
=== Individual Information Privacy ===<br />
The security of an individual's personal information is inextricably tied to their personal privacy. When an individual interacts with other parties using their private information, especially in the online environment, it is hard to guarantee that this information will retain its original integrity. Companies have a legal obligation within the United States to provide the protection of their customers’ personal information during a business transaction, especially when conducted in an online environment.<ref>U.S. Governmental Printing Office: Electronic Code of Federal Regulations http://ecfr.gpoaccess.gov/cgi/t/text/text-idx?c=ecfr&sid=a273032f4545305b53bd3b788739f586&tpl=/ecfrbrowse/Title16/16cfr681_main_02.tpl</ref><br />
<br />
== See Also ==<br />
* [[Cyber Law]]<br />
* [[Information Ethics]]<br />
* [[Information Transparency]]<br />
* [[Online Identity]]<br />
* [[Online Identity Theft]]<br />
* [[Virtual Environment]]<br />
<br />
== References ==<br />
<references/><br />
[[Category:piracy]]<br />
<br />
([[Topics|back to index]])</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Data_Mining&diff=56943Data Mining2016-04-21T19:30:57Z<p>Zzasuwa: /* Use Case #3: Government */</p>
<hr />
<div>[[File:DiagramDataMining.jpg|right|thumb|350px|This illustration shows the role [[Wikipedia:Data Mining|Data Mining]] plays in processing information for business use.]]<br />
'''Data Mining''' is the act of analyzing data from various perspectives and summarizing it into useful information, which combines aspects of artificial intelligence, machine learning, statistics and database systems. Software is implemented as one of many analytical tools used to analyze data. Through data mining, data is presented to users from many different angles, in various categories and relationships. On a more technical term, data mining is the process of realizing correlations or patterns among large fields of relative databases.<ref name="palace">[http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/index.htm Palace, Bill. "What Is Data Mining?" Data Mining. Anderson Graduate School of Management at UCLA, Mar. 1996. Web. 16 Dec. 2011.]</ref><br />
<br />
Data mining is used in many fields such as business, marketing, engineering, medicine, and the music and gaming industry. Businesses employ data mining techniques to gather information on their customers to better advertise and target future customers, while data mining is used to recognize patterns and designate categories for work in gaming, medicine, and music. Because personal information is gathered and studied, ethical concerns arise from the lack of anonymity and privacy of the subjects used in data mining techniques.<br />
<br />
<br />
==Process of Data Mining==<br />
The process of Data Mining requires the extraction of useful information from large sets of data stored in a uniform way, often in a database. The type of information extracted is relative to the type of data available, and the purpose of the data mining project itself – the information extracted could be modeled or represented of the entire set of data for example if the objective was to draw a conclusion of the dataset as a whole. <ref>http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf</ref><br />
<br />
Data mining consists of multiple sub-tasks:<ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Olaru, C., & Wehenkel, L. (1999). Data mining. IEEE Computer Applications in Power, 12(3), 19-25. doi:10.1109/67.773801]</ref><ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Shen Bin, Liu Yuan, & Wang Xiaoyi. (2010). Research on data mining models for the internet of things. 2010 International Conference on Image Analysis and Signal Processing (IASP) (pp. 127-132). Presented at the 2010 International Conference on Image Analysis and Signal Processing (IASP), IEEE. doi:10.1109/IASP.2010.5476146]</ref><br />
<br />
===Anomaly Detection=== <br />
Also called deviation detection, this is the “data cleaning” process where data miners attempt to identify unusual circumstances within the data that may belie anomalies or errors. The process includes program-aided searches of the data, parsing the data through filters that check for certain cases, and searching for unexplainable patterns within the data. The anomaly detection process often requires checking how the data was aggregated, and if human error had an effect on the data present.<br />
<br />
===Clustering=== <br />
[[File:Clustered.png|right|thumb|350px|A database of songs that has been visually represented as clusters of data which best represent the categories of songs present (ie. hip-hop, punk).]]<br />
Situations arise within datasets where there is a desire to know if similarities exist within the data, but no mechanism for detecting them exists. Clustering procedures within data mining aim to find similar objects within the data or to analyze correlations between attributes of the data. Clustering is also used to find topographical similarities within data sets.<br />
<br />
===Classification=== <br />
Use of mined data requires the input and output information to be formatted in a discrete way in order for the information to be useful. Classifying traits of both the input and output of the data minimizes ambiguity, and helps define unique qualities of the data, and the meaning of the data itself. This process also helps discover unique relationships that exist between the data present in the database (the input data), and information that is being presented as conclusive of the data set (the output data). Classification can occur in many formats, from labeling, to building models which fit the particular data set explicitly.<br />
<br />
===Regression=== <br />
The building of a transparent model which fits the data, and adequately conveys the information contained within the data set is the objective of the regression step of data mining. The use of statistical methods is the most common approach within the regression step, because it yields discrete output. Regression modeling also attempts to minimize error within the other steps of the data mining process.<br />
<br />
===Summarization===<br />
This task aims at producing representative descriptions of the data which provides context for the data. The summary process can take multiple formats, the most common of which is numerical analysis, which provides statistical quantification for patterns in the data like the mean or standard deviation found within the data set. These results are often represented in graphical formats – such as histograms or scatter plots. Qualitative data can be summarized by giving a list of trends or frequencies within a set of data. These results are useful because they represent the entirety of the data set in a much more digestible format.<br />
<br />
==Data Mining Tools==<br />
<br />
As data mining is becoming more popular to sift through the massive amounts of data available online, new tools have been cropping up to make the job simpler. Google, for example, has released a free tool called Correlate<ref>http://www.fastcompany.com/1755287/google-correlate-tool-gives-marketers-powerful-new-data-mining-tools</ref> aimed at helping corporations utilize the Google search database to draw conclusions on consumer behavior. MIT and Harvard have recently collaborated in the creation of MINE, a tool of unprecedented power which can analyze more data for more complex patterns than any tool previously available. It is said to approach the quality of human examination in terms of being able to pick up on nonstandard or deceptive patterns<ref>http://www.decodedscience.com/data-mining-tool-advances-mine-ranks-multiple-patterns/7959</ref>.<br />
<br />
==Applications of Data Mining==<br />
===Examples===<br />
A simple example of data mining is analyzing a large population, such as University of Michigan students, and determining simple characteristics that the data has, such as the proportion of the student body that is from each ethnic background.<br />
<br />
Before the term "data mining" came into popular use, many businesses had already implemented its technology. They used powerful computers to comb through quantitative data from supermarket scanners, and analyzed the resulting data for market research purposes. This process have been immensely increasing the precision of analysis, and at the same time decreasing the cost of research.<ref name="palace"/><br />
<br />
===Business===<br />
Data mining is commonly used in business <ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:bookitem&rft.title=Introduction+to+Data+Mining+and+its+Applications&rft.atitle=Data+Mining+in+Sales+Marketing+and+Finance&rft.creator=Sumathi%2C+S&rft.creator=Sivanandam%2C+S+N&rft.series=Studies+in+Computational+Intelligence&rft.date=2006-01-01&rft.pub=Springer+Berlin+Heidelberg&rft.isbn=9783540343509&rft.issn=1860-949X&rft.volume=29&rft.spage=411&rft.epage=438&rft_id=info:doi/10.1007%2F978-3-540-34351-6_18&rft.externalDBID=n%2Fa&rft.externalDocID=978-3-540-34351-6_133505_Chap18 Data Mining in Sales Marketing and Finance. (2006). Introduction to Data Mining and its Applications (Vol. 29, pp. 411-438). Berlin, Heidelberg: Springer Berlin Heidelberg.]</ref> to determine what demographics are buying what products, and to help firms predict customer preferences. Online retailers often track the ways which their clients interact with the content on their site. This leads to an extensive collection of data that can be used by data mining procedures to model representative "personas" of future clients who will visit the site. This has meaningful value for companies, because information generated from data mining practices can lead to more "click-throughs" and potentially, more "conversions" (sales).<br />
<br />
===Science/Academics===<br />
Data Mining also plays a crucial role in scientific analysis. The objective of many large research studies is to find patterns in experimental data. Many data mining procedures allow for applicable modeling of data in a way that is much faster and easier than doing so by hand.<br />
<br />
===Other Uses===<br />
Data Mining techniques have been used in a variety of other matters, such as in admissions processes in universities and political campaigns. <ref>http://communities.washingtontimes.com/neighborhood/political-potpourri/2012/nov/17/how-obama-won/</ref><br />
<ref>http://www.tnr.com/article/110862/data-mining-the-ineffective-future-affirmative-action-in-education#</ref> <br />
<br />
==Ethical Implications==<br />
Data mining is an inherently morally-neutral action, as it is simply the practice of working with large stored data sets and the process itself does not account for the way that the data was generated or will inevitably be used. The ethics of data mining come under scrutiny when the type of data being mined is something that the individuals or specific entities subject to the mining feel should not be disclosed for others to view. Issues also arise when sufficiently robust restrictions are not in place to hide the private characteristics of an individual’s personal identity in the online environment. Likewise, when firms neglect to take adequately tested precautionary measures for protecting a customer’s data as part of a larger collective set of consumer information, they are effectively opening all their clients’ data up to the possibility of unprotected digital consumption and exploitation.<br />
<br />
===User Profiling and Identification===<br />
In an attempt to build accurate models, data miners in online environments occasionally utilize the personal information of individuals who visit their sites. The information generally collected includes, but is not limited to, geographic identifiers, records of past behavior, or information that can identify an individual by a range of other intimate attributes like user account details or IP addresses. Many users do not actively realize that their information is being collected and many question the ethics of using an individual’s private information without their outright and formal consent.<ref>http://www.ecommercetimes.com/story/52616.html</ref> Additionally, data mining practices and tools have made the automated profiling of groups and individuals an easy task for a growing number of the technically literate. Using data mining to create customer profiling questions also raises ethical concerns regarding these practices due to the associated risks it creates for discrimination, de-individualization, and information asymmetries.<ref>Schermer, B.W. (2011). The limits of privacy in automated profiling and data mining. Computer Law and Security Report, 27(1), pp. 45-52.</ref><br />
<br />
===Distortion of Truth===<br />
Second to privacy concerns, distorting or misrepresenting trends in data that are not actually there together account for the other main ethical issue on the topic of data mining.<ref>Data Mining: Study Guide. (nd). North Carolina State University. Retrieved from https://ethics.csc.ncsu.edu/privacy/mining/study.php</ref> The result of human or programmatic mishaps, generating flawed associations between data points can be severely costly to reputations, economic footings, and the continued confidence in the reliability of the data analysis method.<br />
<br />
===Anonymity and Ownership===<br />
Many information systems contain data on individuals, so maintaining anonymity of the data and informing those whose information is being used, exactly how it is used and to what effect, is not only important but in many cases a legally binding matter. Allowing individuals the freedom to opt in or opt out of data mining processes is one way to ensure some form of ethical responsibility.<br />
<br />
Discussion regarding the role of data ownership emerges as a natural consequence to its collection from disparate sources and its subsequent composite processing, analysis, and summarization. A contested point in some circumstances is the differentiation between raw source data (input) and the proprietary information product (output). Because the former, when in aggregate form, represents the constituent building blocks of the latter, the resulting interdependency spawns many debates surrounding who, if anyone, rightfully owns each intermediate information good and the finished analysis. Boiling down to where lines are drawn with respect to voluntary (or involuntary) data relinquishment and the perception of residual value, the ground is ripe for dispute and ethical ambiguity.<br />
<br />
===Use Case #1: Social Networking===<br />
Mining data found on social networking sites poses a particular challenge, as a great deal of personal information can be found on individuals’ social media pages. It is not uncommon for social media firms like Facebook and Twitter to sell the information mined from users’ personal pages to advertisers and academic researchers. Despite the fact that this data is anonymized and/or collected from a large pool of public-facing accounts, there are still a number of valid sources for apprehension regarding its execution and overall legitimacy.<br />
<br />
To reduce users’ qualms surrounding this type of activity and to ensure that the ethical validity of all such experimentation meets a sound moral standard, committees in the form of [[Wikipedia:Institutional_review_board|institutional review boards]] (IRBs) are organized across both industry and the social science disciplines of higher-learning. They are entrusted with the responsibility for reviewing, monitoring, and approving of any biomedical or behavioral research involving humans.<ref>Wikipedia: Institutional review board https://en.wikipedia.org/wiki/Institutional_review_board</ref> Because the natural extension of examining humans’ behavioral tendencies places us in the digital realm and thus satisfies the need for the aforementioned requisite oversight, here too IRBs serve a vital role in fulfilling the third-party ethical assessment of data collection and data manipulation for the sake of research driven advertising or academic inquiry.<br />
<br />
Firms often explicitly state in the terms and conditions of using their sites that they reserve the right to sell or use an individual's personally volunteered information toward serving their own business needs. Although this activity is controlled and kept within defined limits by consumer privacy laws, vigilant monitoring is necessary by the [[Wikipedia:Federal_Trade_Commission|Federal Trade Commission]] (FTC) and other such organizations to ensure that businesses are conducting themselves both fairly and lawfully. Because a user agreement is usually only displayed once (typically during the registration process), if it all, the gravity and seriousness ascribed to its contents is frequently undervalued by users, often relegated as unimportant and a low priority.<br />
<br />
As a majority of avid social media users are only concerned about a platform’s (superficially free) software and community assets -- expressed through its ability to cultivate social capital or the manner by which it advocates for a “more open and connected world” (e.g. Facebook) -- the embedded costs posed by data mining to users are subliminal and certainly not without their ramifications.<ref>Facebook, [http://www.facebook.com/advertising/ Advertising]</ref> Many users feel that the pursuit is an irresponsible practice, irrespective of the safeguards and stipulations found in user agreements, and consider it to be an activity in which social media firms should not engage.<ref>http://mashable.com/2011/02/25/data-mining-social-marketing/</ref> Often for that reason, or purely on the rationale that the sum total of one’s social contributions could be aggregated and packaged up for sale at any time to an outside party -- without due notification or option for opting out -- many people who choose not to participate on Web-based social platforms due so for concerns that mirror those convictions. Increasing the transparency of what data is mined and identifying when a user is particularly vulnerable (i.e. for disregarding privacy measures) has the potential to restore the necessary confidence in weary users.<br />
<br />
===Use Case #2: Business===<br />
Much like the value that can be derived from mining social sites, a wide array of businesses have a similar opportunity in leveraging data mining techniques for improving their respective processes and ultimately, economic returns. Reducing expenses and maximizing profits is a shared challenge among for-profit institutions. As a result, it is not uncommon for corporations spanning various disciplines to use mined data in conjunction with analytic and predictive methods for getting a more informed business perspective. Ethically, many of the issues that confront businesses with respect to data mining are similar to other contextual use cases: they revolve around the notion of privacy and the extent to which the resulting information can be shared with third-parties. Because the benefits of such activity can be used to “...reduce fraud, anticipate resource demand, increase acquisition and curb customer attrition,” the attractive nature of the process generally outweighs any lingering moral uncertainties.<ref>SAS Institute. (2003). SAS data mining tools drive growing segment of business intelligence market [Press release]. Retrieved from http://www.itweb.co.za/office/sas/PressRelease.php?StoryID=135244</ref><br />
<br />
===Use Case #3: Government===<br />
Privacy is a theme that pertains to almost every possible application of data mining as a mechanism for revealing information. This happens to be no different when discussed in the framework of government utilization. There are hundreds of available uses, with some of the most recently relevant being in the realm of predicting and and preventing acts of terrorism. The broad and sweeping nature of data mining, especially in the pursuit of foreign national intelligence acquisition, can have unintended effects despite its many arguably useful implementations for combating terror and crimes against humanity. Among others issues, this exceptionally thorough process has been proven to threaten the privacy of United States citizens who, without an otherwise legitimate underlying connection, were mined and found to falsely possess direct ties to criminal or extremist organizations. Contingently included in the mining as a result of accidental or indirect associations, the incorrect isolation and thus the practice itself -- predicated on a more is better than less mentality -- can have dangerous repercussions when not safely managed and implemented.<ref>Harris, Shane. Army project illustrates promise, shortcomings of data mining. (2005). Government Executive. Retrieved from http://www.govexec.com/defense/2005/12/army-project-illustrates-promise-shortcomings-of-data-mining/20758/</ref> Furthermore, the construction of possibly erroneous correlative models built from the massive accumulation of mined data may, in some circumstances, display a statistically significant relationship where one does not actually exist. A pitfall of data analysis on a super large sample is that nearly any difference in quantitative measure, even minor, has the potential to reflect a casual association without a realistic or practical underpinning.<ref>Helberg, Clay (1996). Pitfalls of data analysis. Practical Assessment, Research & Evaluation, 5(5). Retrieved April 21, 2016 from http://PAREonline.net/getvn.asp?v=5&n=5</ref> The potential for flawed outcomes must be taken into active consideration when drawing conclusions from comprehensive data mining activities and analysis.<br />
<br />
===Use Case #4: Healthcare===<br />
As more information related to individuals’ health records becomes digitized, data mining will become significantly more useful within the healthcare field. Patients’ health records, when used in aggregate, will provide significant insight into the causes of specific illnesses. The success of data mining in this context hinges on the process of converting significant amounts of data into standardized digital formats on software systems with low storage and search costs.<br />
<br />
==See also==<br />
* [[Data Aggregation Online]]<br />
* [[Targeted Advertising (Online)]]<br />
* [[Geographic Information Systems]]<br />
*[[Recommender Systems]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Concepts]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Data_Mining&diff=56942Data Mining2016-04-21T19:27:44Z<p>Zzasuwa: /* Distortion of Truth */</p>
<hr />
<div>[[File:DiagramDataMining.jpg|right|thumb|350px|This illustration shows the role [[Wikipedia:Data Mining|Data Mining]] plays in processing information for business use.]]<br />
'''Data Mining''' is the act of analyzing data from various perspectives and summarizing it into useful information, which combines aspects of artificial intelligence, machine learning, statistics and database systems. Software is implemented as one of many analytical tools used to analyze data. Through data mining, data is presented to users from many different angles, in various categories and relationships. On a more technical term, data mining is the process of realizing correlations or patterns among large fields of relative databases.<ref name="palace">[http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/index.htm Palace, Bill. "What Is Data Mining?" Data Mining. Anderson Graduate School of Management at UCLA, Mar. 1996. Web. 16 Dec. 2011.]</ref><br />
<br />
Data mining is used in many fields such as business, marketing, engineering, medicine, and the music and gaming industry. Businesses employ data mining techniques to gather information on their customers to better advertise and target future customers, while data mining is used to recognize patterns and designate categories for work in gaming, medicine, and music. Because personal information is gathered and studied, ethical concerns arise from the lack of anonymity and privacy of the subjects used in data mining techniques.<br />
<br />
<br />
==Process of Data Mining==<br />
The process of Data Mining requires the extraction of useful information from large sets of data stored in a uniform way, often in a database. The type of information extracted is relative to the type of data available, and the purpose of the data mining project itself – the information extracted could be modeled or represented of the entire set of data for example if the objective was to draw a conclusion of the dataset as a whole. <ref>http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf</ref><br />
<br />
Data mining consists of multiple sub-tasks:<ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Olaru, C., & Wehenkel, L. (1999). Data mining. IEEE Computer Applications in Power, 12(3), 19-25. doi:10.1109/67.773801]</ref><ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Shen Bin, Liu Yuan, & Wang Xiaoyi. (2010). Research on data mining models for the internet of things. 2010 International Conference on Image Analysis and Signal Processing (IASP) (pp. 127-132). Presented at the 2010 International Conference on Image Analysis and Signal Processing (IASP), IEEE. doi:10.1109/IASP.2010.5476146]</ref><br />
<br />
===Anomaly Detection=== <br />
Also called deviation detection, this is the “data cleaning” process where data miners attempt to identify unusual circumstances within the data that may belie anomalies or errors. The process includes program-aided searches of the data, parsing the data through filters that check for certain cases, and searching for unexplainable patterns within the data. The anomaly detection process often requires checking how the data was aggregated, and if human error had an effect on the data present.<br />
<br />
===Clustering=== <br />
[[File:Clustered.png|right|thumb|350px|A database of songs that has been visually represented as clusters of data which best represent the categories of songs present (ie. hip-hop, punk).]]<br />
Situations arise within datasets where there is a desire to know if similarities exist within the data, but no mechanism for detecting them exists. Clustering procedures within data mining aim to find similar objects within the data or to analyze correlations between attributes of the data. Clustering is also used to find topographical similarities within data sets.<br />
<br />
===Classification=== <br />
Use of mined data requires the input and output information to be formatted in a discrete way in order for the information to be useful. Classifying traits of both the input and output of the data minimizes ambiguity, and helps define unique qualities of the data, and the meaning of the data itself. This process also helps discover unique relationships that exist between the data present in the database (the input data), and information that is being presented as conclusive of the data set (the output data). Classification can occur in many formats, from labeling, to building models which fit the particular data set explicitly.<br />
<br />
===Regression=== <br />
The building of a transparent model which fits the data, and adequately conveys the information contained within the data set is the objective of the regression step of data mining. The use of statistical methods is the most common approach within the regression step, because it yields discrete output. Regression modeling also attempts to minimize error within the other steps of the data mining process.<br />
<br />
===Summarization===<br />
This task aims at producing representative descriptions of the data which provides context for the data. The summary process can take multiple formats, the most common of which is numerical analysis, which provides statistical quantification for patterns in the data like the mean or standard deviation found within the data set. These results are often represented in graphical formats – such as histograms or scatter plots. Qualitative data can be summarized by giving a list of trends or frequencies within a set of data. These results are useful because they represent the entirety of the data set in a much more digestible format.<br />
<br />
==Data Mining Tools==<br />
<br />
As data mining is becoming more popular to sift through the massive amounts of data available online, new tools have been cropping up to make the job simpler. Google, for example, has released a free tool called Correlate<ref>http://www.fastcompany.com/1755287/google-correlate-tool-gives-marketers-powerful-new-data-mining-tools</ref> aimed at helping corporations utilize the Google search database to draw conclusions on consumer behavior. MIT and Harvard have recently collaborated in the creation of MINE, a tool of unprecedented power which can analyze more data for more complex patterns than any tool previously available. It is said to approach the quality of human examination in terms of being able to pick up on nonstandard or deceptive patterns<ref>http://www.decodedscience.com/data-mining-tool-advances-mine-ranks-multiple-patterns/7959</ref>.<br />
<br />
==Applications of Data Mining==<br />
===Examples===<br />
A simple example of data mining is analyzing a large population, such as University of Michigan students, and determining simple characteristics that the data has, such as the proportion of the student body that is from each ethnic background.<br />
<br />
Before the term "data mining" came into popular use, many businesses had already implemented its technology. They used powerful computers to comb through quantitative data from supermarket scanners, and analyzed the resulting data for market research purposes. This process have been immensely increasing the precision of analysis, and at the same time decreasing the cost of research.<ref name="palace"/><br />
<br />
===Business===<br />
Data mining is commonly used in business <ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:bookitem&rft.title=Introduction+to+Data+Mining+and+its+Applications&rft.atitle=Data+Mining+in+Sales+Marketing+and+Finance&rft.creator=Sumathi%2C+S&rft.creator=Sivanandam%2C+S+N&rft.series=Studies+in+Computational+Intelligence&rft.date=2006-01-01&rft.pub=Springer+Berlin+Heidelberg&rft.isbn=9783540343509&rft.issn=1860-949X&rft.volume=29&rft.spage=411&rft.epage=438&rft_id=info:doi/10.1007%2F978-3-540-34351-6_18&rft.externalDBID=n%2Fa&rft.externalDocID=978-3-540-34351-6_133505_Chap18 Data Mining in Sales Marketing and Finance. (2006). Introduction to Data Mining and its Applications (Vol. 29, pp. 411-438). Berlin, Heidelberg: Springer Berlin Heidelberg.]</ref> to determine what demographics are buying what products, and to help firms predict customer preferences. Online retailers often track the ways which their clients interact with the content on their site. This leads to an extensive collection of data that can be used by data mining procedures to model representative "personas" of future clients who will visit the site. This has meaningful value for companies, because information generated from data mining practices can lead to more "click-throughs" and potentially, more "conversions" (sales).<br />
<br />
===Science/Academics===<br />
Data Mining also plays a crucial role in scientific analysis. The objective of many large research studies is to find patterns in experimental data. Many data mining procedures allow for applicable modeling of data in a way that is much faster and easier than doing so by hand.<br />
<br />
===Other Uses===<br />
Data Mining techniques have been used in a variety of other matters, such as in admissions processes in universities and political campaigns. <ref>http://communities.washingtontimes.com/neighborhood/political-potpourri/2012/nov/17/how-obama-won/</ref><br />
<ref>http://www.tnr.com/article/110862/data-mining-the-ineffective-future-affirmative-action-in-education#</ref> <br />
<br />
==Ethical Implications==<br />
Data mining is an inherently morally-neutral action, as it is simply the practice of working with large stored data sets and the process itself does not account for the way that the data was generated or will inevitably be used. The ethics of data mining come under scrutiny when the type of data being mined is something that the individuals or specific entities subject to the mining feel should not be disclosed for others to view. Issues also arise when sufficiently robust restrictions are not in place to hide the private characteristics of an individual’s personal identity in the online environment. Likewise, when firms neglect to take adequately tested precautionary measures for protecting a customer’s data as part of a larger collective set of consumer information, they are effectively opening all their clients’ data up to the possibility of unprotected digital consumption and exploitation.<br />
<br />
===User Profiling and Identification===<br />
In an attempt to build accurate models, data miners in online environments occasionally utilize the personal information of individuals who visit their sites. The information generally collected includes, but is not limited to, geographic identifiers, records of past behavior, or information that can identify an individual by a range of other intimate attributes like user account details or IP addresses. Many users do not actively realize that their information is being collected and many question the ethics of using an individual’s private information without their outright and formal consent.<ref>http://www.ecommercetimes.com/story/52616.html</ref> Additionally, data mining practices and tools have made the automated profiling of groups and individuals an easy task for a growing number of the technically literate. Using data mining to create customer profiling questions also raises ethical concerns regarding these practices due to the associated risks it creates for discrimination, de-individualization, and information asymmetries.<ref>Schermer, B.W. (2011). The limits of privacy in automated profiling and data mining. Computer Law and Security Report, 27(1), pp. 45-52.</ref><br />
<br />
===Distortion of Truth===<br />
Second to privacy concerns, distorting or misrepresenting trends in data that are not actually there together account for the other main ethical issue on the topic of data mining.<ref>Data Mining: Study Guide. (nd). North Carolina State University. Retrieved from https://ethics.csc.ncsu.edu/privacy/mining/study.php</ref> The result of human or programmatic mishaps, generating flawed associations between data points can be severely costly to reputations, economic footings, and the continued confidence in the reliability of the data analysis method.<br />
<br />
===Anonymity and Ownership===<br />
Many information systems contain data on individuals, so maintaining anonymity of the data and informing those whose information is being used, exactly how it is used and to what effect, is not only important but in many cases a legally binding matter. Allowing individuals the freedom to opt in or opt out of data mining processes is one way to ensure some form of ethical responsibility.<br />
<br />
Discussion regarding the role of data ownership emerges as a natural consequence to its collection from disparate sources and its subsequent composite processing, analysis, and summarization. A contested point in some circumstances is the differentiation between raw source data (input) and the proprietary information product (output). Because the former, when in aggregate form, represents the constituent building blocks of the latter, the resulting interdependency spawns many debates surrounding who, if anyone, rightfully owns each intermediate information good and the finished analysis. Boiling down to where lines are drawn with respect to voluntary (or involuntary) data relinquishment and the perception of residual value, the ground is ripe for dispute and ethical ambiguity.<br />
<br />
===Use Case #1: Social Networking===<br />
Mining data found on social networking sites poses a particular challenge, as a great deal of personal information can be found on individuals’ social media pages. It is not uncommon for social media firms like Facebook and Twitter to sell the information mined from users’ personal pages to advertisers and academic researchers. Despite the fact that this data is anonymized and/or collected from a large pool of public-facing accounts, there are still a number of valid sources for apprehension regarding its execution and overall legitimacy.<br />
<br />
To reduce users’ qualms surrounding this type of activity and to ensure that the ethical validity of all such experimentation meets a sound moral standard, committees in the form of [[Wikipedia:Institutional_review_board|institutional review boards]] (IRBs) are organized across both industry and the social science disciplines of higher-learning. They are entrusted with the responsibility for reviewing, monitoring, and approving of any biomedical or behavioral research involving humans.<ref>Wikipedia: Institutional review board https://en.wikipedia.org/wiki/Institutional_review_board</ref> Because the natural extension of examining humans’ behavioral tendencies places us in the digital realm and thus satisfies the need for the aforementioned requisite oversight, here too IRBs serve a vital role in fulfilling the third-party ethical assessment of data collection and data manipulation for the sake of research driven advertising or academic inquiry.<br />
<br />
Firms often explicitly state in the terms and conditions of using their sites that they reserve the right to sell or use an individual's personally volunteered information toward serving their own business needs. Although this activity is controlled and kept within defined limits by consumer privacy laws, vigilant monitoring is necessary by the [[Wikipedia:Federal_Trade_Commission|Federal Trade Commission]] (FTC) and other such organizations to ensure that businesses are conducting themselves both fairly and lawfully. Because a user agreement is usually only displayed once (typically during the registration process), if it all, the gravity and seriousness ascribed to its contents is frequently undervalued by users, often relegated as unimportant and a low priority.<br />
<br />
As a majority of avid social media users are only concerned about a platform’s (superficially free) software and community assets -- expressed through its ability to cultivate social capital or the manner by which it advocates for a “more open and connected world” (e.g. Facebook) -- the embedded costs posed by data mining to users are subliminal and certainly not without their ramifications.<ref>Facebook, [http://www.facebook.com/advertising/ Advertising]</ref> Many users feel that the pursuit is an irresponsible practice, irrespective of the safeguards and stipulations found in user agreements, and consider it to be an activity in which social media firms should not engage.<ref>http://mashable.com/2011/02/25/data-mining-social-marketing/</ref> Often for that reason, or purely on the rationale that the sum total of one’s social contributions could be aggregated and packaged up for sale at any time to an outside party -- without due notification or option for opting out -- many people who choose not to participate on Web-based social platforms due so for concerns that mirror those convictions. Increasing the transparency of what data is mined and identifying when a user is particularly vulnerable (i.e. for disregarding privacy measures) has the potential to restore the necessary confidence in weary users.<br />
<br />
===Use Case #2: Business===<br />
Much like the value that can be derived from mining social sites, a wide array of businesses have a similar opportunity in leveraging data mining techniques for improving their respective processes and ultimately, economic returns. Reducing expenses and maximizing profits is a shared challenge among for-profit institutions. As a result, it is not uncommon for corporations spanning various disciplines to use mined data in conjunction with analytic and predictive methods for getting a more informed business perspective. Ethically, many of the issues that confront businesses with respect to data mining are similar to other contextual use cases: they revolve around the notion of privacy and the extent to which the resulting information can be shared with third-parties. Because the benefits of such activity can be used to “...reduce fraud, anticipate resource demand, increase acquisition and curb customer attrition,” the attractive nature of the process generally outweighs any lingering moral uncertainties.<ref>SAS Institute. (2003). SAS data mining tools drive growing segment of business intelligence market [Press release]. Retrieved from http://www.itweb.co.za/office/sas/PressRelease.php?StoryID=135244</ref><br />
<br />
===Use Case #3: Government===<br />
Privacy is a theme that pertains to almost every possible application of data mining as a mechanism for revealing information. This happens to be no different when discussed in the framework of government utilization. There are hundreds of available uses, with some of the most recently relevant being in the realm of predicting and and preventing acts of terrorism. The broad and sweeping nature of data mining, especially in the pursuit of foreign national intelligence acquisition, can have unintended effects despite its many arguably useful implementations for combating terror and crimes against humanity. Among others issues, this exceptionally thorough process has been proven to threaten the privacy of United States citizens who, without an otherwise legitimate underlying connection, were mined and found to falsely possess direct ties to criminal or extremist organizations. Contingently included in the mining as a result of accidental or indirect associations, the incorrect isolation and thus the practice itself -- predicated on a more is better than less mentality -- can have dangerous repercussions when not safely managed and implemented.<ref>http://www.govexec.com/defense/2005/12/army-project-illustrates-promise-shortcomings-of-data-mining/20758/</ref> Furthermore, the construction of possibly erroneous correlative models built from the massive accumulation of mined data may, in some circumstances, display a statistically significant relationship where one does not actually exist. A pitfall of data analysis on a super large sample is that nearly any difference in quantitative measure, even minor, has the potential to reflect a casual association without a realistic or practical underpinning.<ref>Helberg, Clay (1996). Pitfalls of data analysis. Practical Assessment, Research & Evaluation, 5(5). Retrieved April 21, 2016 from http://PAREonline.net/getvn.asp?v=5&n=5</ref> The potential for flawed outcomes must be taken into active consideration when drawing conclusions from comprehensive data mining activities and analysis.<br />
<br />
===Use Case #4: Healthcare===<br />
As more information related to individuals’ health records becomes digitized, data mining will become significantly more useful within the healthcare field. Patients’ health records, when used in aggregate, will provide significant insight into the causes of specific illnesses. The success of data mining in this context hinges on the process of converting significant amounts of data into standardized digital formats on software systems with low storage and search costs.<br />
<br />
==See also==<br />
* [[Data Aggregation Online]]<br />
* [[Targeted Advertising (Online)]]<br />
* [[Geographic Information Systems]]<br />
*[[Recommender Systems]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Concepts]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Data_Mining&diff=56939Data Mining2016-04-21T19:18:27Z<p>Zzasuwa: /* Use Case #3: Government */</p>
<hr />
<div>[[File:DiagramDataMining.jpg|right|thumb|350px|This illustration shows the role [[Wikipedia:Data Mining|Data Mining]] plays in processing information for business use.]]<br />
'''Data Mining''' is the act of analyzing data from various perspectives and summarizing it into useful information, which combines aspects of artificial intelligence, machine learning, statistics and database systems. Software is implemented as one of many analytical tools used to analyze data. Through data mining, data is presented to users from many different angles, in various categories and relationships. On a more technical term, data mining is the process of realizing correlations or patterns among large fields of relative databases.<ref name="palace">[http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/index.htm Palace, Bill. "What Is Data Mining?" Data Mining. Anderson Graduate School of Management at UCLA, Mar. 1996. Web. 16 Dec. 2011.]</ref><br />
<br />
Data mining is used in many fields such as business, marketing, engineering, medicine, and the music and gaming industry. Businesses employ data mining techniques to gather information on their customers to better advertise and target future customers, while data mining is used to recognize patterns and designate categories for work in gaming, medicine, and music. Because personal information is gathered and studied, ethical concerns arise from the lack of anonymity and privacy of the subjects used in data mining techniques.<br />
<br />
<br />
==Process of Data Mining==<br />
The process of Data Mining requires the extraction of useful information from large sets of data stored in a uniform way, often in a database. The type of information extracted is relative to the type of data available, and the purpose of the data mining project itself – the information extracted could be modeled or represented of the entire set of data for example if the objective was to draw a conclusion of the dataset as a whole. <ref>http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf</ref><br />
<br />
Data mining consists of multiple sub-tasks:<ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Olaru, C., & Wehenkel, L. (1999). Data mining. IEEE Computer Applications in Power, 12(3), 19-25. doi:10.1109/67.773801]</ref><ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Shen Bin, Liu Yuan, & Wang Xiaoyi. (2010). Research on data mining models for the internet of things. 2010 International Conference on Image Analysis and Signal Processing (IASP) (pp. 127-132). Presented at the 2010 International Conference on Image Analysis and Signal Processing (IASP), IEEE. doi:10.1109/IASP.2010.5476146]</ref><br />
<br />
===Anomaly Detection=== <br />
Also called deviation detection, this is the “data cleaning” process where data miners attempt to identify unusual circumstances within the data that may belie anomalies or errors. The process includes program-aided searches of the data, parsing the data through filters that check for certain cases, and searching for unexplainable patterns within the data. The anomaly detection process often requires checking how the data was aggregated, and if human error had an effect on the data present.<br />
<br />
===Clustering=== <br />
[[File:Clustered.png|right|thumb|350px|A database of songs that has been visually represented as clusters of data which best represent the categories of songs present (ie. hip-hop, punk).]]<br />
Situations arise within datasets where there is a desire to know if similarities exist within the data, but no mechanism for detecting them exists. Clustering procedures within data mining aim to find similar objects within the data or to analyze correlations between attributes of the data. Clustering is also used to find topographical similarities within data sets.<br />
<br />
===Classification=== <br />
Use of mined data requires the input and output information to be formatted in a discrete way in order for the information to be useful. Classifying traits of both the input and output of the data minimizes ambiguity, and helps define unique qualities of the data, and the meaning of the data itself. This process also helps discover unique relationships that exist between the data present in the database (the input data), and information that is being presented as conclusive of the data set (the output data). Classification can occur in many formats, from labeling, to building models which fit the particular data set explicitly.<br />
<br />
===Regression=== <br />
The building of a transparent model which fits the data, and adequately conveys the information contained within the data set is the objective of the regression step of data mining. The use of statistical methods is the most common approach within the regression step, because it yields discrete output. Regression modeling also attempts to minimize error within the other steps of the data mining process.<br />
<br />
===Summarization===<br />
This task aims at producing representative descriptions of the data which provides context for the data. The summary process can take multiple formats, the most common of which is numerical analysis, which provides statistical quantification for patterns in the data like the mean or standard deviation found within the data set. These results are often represented in graphical formats – such as histograms or scatter plots. Qualitative data can be summarized by giving a list of trends or frequencies within a set of data. These results are useful because they represent the entirety of the data set in a much more digestible format.<br />
<br />
==Data Mining Tools==<br />
<br />
As data mining is becoming more popular to sift through the massive amounts of data available online, new tools have been cropping up to make the job simpler. Google, for example, has released a free tool called Correlate<ref>http://www.fastcompany.com/1755287/google-correlate-tool-gives-marketers-powerful-new-data-mining-tools</ref> aimed at helping corporations utilize the Google search database to draw conclusions on consumer behavior. MIT and Harvard have recently collaborated in the creation of MINE, a tool of unprecedented power which can analyze more data for more complex patterns than any tool previously available. It is said to approach the quality of human examination in terms of being able to pick up on nonstandard or deceptive patterns<ref>http://www.decodedscience.com/data-mining-tool-advances-mine-ranks-multiple-patterns/7959</ref>.<br />
<br />
==Applications of Data Mining==<br />
===Examples===<br />
A simple example of data mining is analyzing a large population, such as University of Michigan students, and determining simple characteristics that the data has, such as the proportion of the student body that is from each ethnic background.<br />
<br />
Before the term "data mining" came into popular use, many businesses had already implemented its technology. They used powerful computers to comb through quantitative data from supermarket scanners, and analyzed the resulting data for market research purposes. This process have been immensely increasing the precision of analysis, and at the same time decreasing the cost of research.<ref name="palace"/><br />
<br />
===Business===<br />
Data mining is commonly used in business <ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:bookitem&rft.title=Introduction+to+Data+Mining+and+its+Applications&rft.atitle=Data+Mining+in+Sales+Marketing+and+Finance&rft.creator=Sumathi%2C+S&rft.creator=Sivanandam%2C+S+N&rft.series=Studies+in+Computational+Intelligence&rft.date=2006-01-01&rft.pub=Springer+Berlin+Heidelberg&rft.isbn=9783540343509&rft.issn=1860-949X&rft.volume=29&rft.spage=411&rft.epage=438&rft_id=info:doi/10.1007%2F978-3-540-34351-6_18&rft.externalDBID=n%2Fa&rft.externalDocID=978-3-540-34351-6_133505_Chap18 Data Mining in Sales Marketing and Finance. (2006). Introduction to Data Mining and its Applications (Vol. 29, pp. 411-438). Berlin, Heidelberg: Springer Berlin Heidelberg.]</ref> to determine what demographics are buying what products, and to help firms predict customer preferences. Online retailers often track the ways which their clients interact with the content on their site. This leads to an extensive collection of data that can be used by data mining procedures to model representative "personas" of future clients who will visit the site. This has meaningful value for companies, because information generated from data mining practices can lead to more "click-throughs" and potentially, more "conversions" (sales).<br />
<br />
===Science/Academics===<br />
Data Mining also plays a crucial role in scientific analysis. The objective of many large research studies is to find patterns in experimental data. Many data mining procedures allow for applicable modeling of data in a way that is much faster and easier than doing so by hand.<br />
<br />
===Other Uses===<br />
Data Mining techniques have been used in a variety of other matters, such as in admissions processes in universities and political campaigns. <ref>http://communities.washingtontimes.com/neighborhood/political-potpourri/2012/nov/17/how-obama-won/</ref><br />
<ref>http://www.tnr.com/article/110862/data-mining-the-ineffective-future-affirmative-action-in-education#</ref> <br />
<br />
==Ethical Implications==<br />
Data mining is an inherently morally-neutral action, as it is simply the practice of working with large stored data sets and the process itself does not account for the way that the data was generated or will inevitably be used. The ethics of data mining come under scrutiny when the type of data being mined is something that the individuals or specific entities subject to the mining feel should not be disclosed for others to view. Issues also arise when sufficiently robust restrictions are not in place to hide the private characteristics of an individual’s personal identity in the online environment. Likewise, when firms neglect to take adequately tested precautionary measures for protecting a customer’s data as part of a larger collective set of consumer information, they are effectively opening all their clients’ data up to the possibility of unprotected digital consumption and exploitation.<br />
<br />
===User Profiling and Identification===<br />
In an attempt to build accurate models, data miners in online environments occasionally utilize the personal information of individuals who visit their sites. The information generally collected includes, but is not limited to, geographic identifiers, records of past behavior, or information that can identify an individual by a range of other intimate attributes like user account details or IP addresses. Many users do not actively realize that their information is being collected and many question the ethics of using an individual’s private information without their outright and formal consent.<ref>http://www.ecommercetimes.com/story/52616.html</ref> Additionally, data mining practices and tools have made the automated profiling of groups and individuals an easy task for a growing number of the technically literate. Using data mining to create customer profiling questions also raises ethical concerns regarding these practices due to the associated risks it creates for discrimination, de-individualization, and information asymmetries.<ref>Schermer, B.W. (2011). The limits of privacy in automated profiling and data mining. Computer Law and Security Report, 27(1), pp. 45-52.</ref><br />
<br />
===Distortion of Truth===<br />
Second to privacy concerns, distorting or misrepresenting trends in data that are not actually there together account for the other main ethical issue on the topic of data mining. The result of human or programmatic mishaps, generating flawed associations between data points can be severely costly to reputations, economic footings, and the continued confidence in the reliability of the data analysis method.<br />
<br />
===Anonymity and Ownership===<br />
Many information systems contain data on individuals, so maintaining anonymity of the data and informing those whose information is being used, exactly how it is used and to what effect, is not only important but in many cases a legally binding matter. Allowing individuals the freedom to opt in or opt out of data mining processes is one way to ensure some form of ethical responsibility.<br />
<br />
Discussion regarding the role of data ownership emerges as a natural consequence to its collection from disparate sources and its subsequent composite processing, analysis, and summarization. A contested point in some circumstances is the differentiation between raw source data (input) and the proprietary information product (output). Because the former, when in aggregate form, represents the constituent building blocks of the latter, the resulting interdependency spawns many debates surrounding who, if anyone, rightfully owns each intermediate information good and the finished analysis. Boiling down to where lines are drawn with respect to voluntary (or involuntary) data relinquishment and the perception of residual value, the ground is ripe for dispute and ethical ambiguity.<br />
<br />
===Use Case #1: Social Networking===<br />
Mining data found on social networking sites poses a particular challenge, as a great deal of personal information can be found on individuals’ social media pages. It is not uncommon for social media firms like Facebook and Twitter to sell the information mined from users’ personal pages to advertisers and academic researchers. Despite the fact that this data is anonymized and/or collected from a large pool of public-facing accounts, there are still a number of valid sources for apprehension regarding its execution and overall legitimacy.<br />
<br />
To reduce users’ qualms surrounding this type of activity and to ensure that the ethical validity of all such experimentation meets a sound moral standard, committees in the form of [[Wikipedia:Institutional_review_board|institutional review boards]] (IRBs) are organized across both industry and the social science disciplines of higher-learning. They are entrusted with the responsibility for reviewing, monitoring, and approving of any biomedical or behavioral research involving humans.<ref>Wikipedia: Institutional review board https://en.wikipedia.org/wiki/Institutional_review_board</ref> Because the natural extension of examining humans’ behavioral tendencies places us in the digital realm and thus satisfies the need for the aforementioned requisite oversight, here too IRBs serve a vital role in fulfilling the third-party ethical assessment of data collection and data manipulation for the sake of research driven advertising or academic inquiry.<br />
<br />
Firms often explicitly state in the terms and conditions of using their sites that they reserve the right to sell or use an individual's personally volunteered information toward serving their own business needs. Although this activity is controlled and kept within defined limits by consumer privacy laws, vigilant monitoring is necessary by the [[Wikipedia:Federal_Trade_Commission|Federal Trade Commission]] (FTC) and other such organizations to ensure that businesses are conducting themselves both fairly and lawfully. Because a user agreement is usually only displayed once (typically during the registration process), if it all, the gravity and seriousness ascribed to its contents is frequently undervalued by users, often relegated as unimportant and a low priority.<br />
<br />
As a majority of avid social media users are only concerned about a platform’s (superficially free) software and community assets -- expressed through its ability to cultivate social capital or the manner by which it advocates for a “more open and connected world” (e.g. Facebook) -- the embedded costs posed by data mining to users are subliminal and certainly not without their ramifications.<ref>Facebook, [http://www.facebook.com/advertising/ Advertising]</ref> Many users feel that the pursuit is an irresponsible practice, irrespective of the safeguards and stipulations found in user agreements, and consider it to be an activity in which social media firms should not engage.<ref>http://mashable.com/2011/02/25/data-mining-social-marketing/</ref> Often for that reason, or purely on the rationale that the sum total of one’s social contributions could be aggregated and packaged up for sale at any time to an outside party -- without due notification or option for opting out -- many people who choose not to participate on Web-based social platforms due so for concerns that mirror those convictions. Increasing the transparency of what data is mined and identifying when a user is particularly vulnerable (i.e. for disregarding privacy measures) has the potential to restore the necessary confidence in weary users.<br />
<br />
===Use Case #2: Business===<br />
Much like the value that can be derived from mining social sites, a wide array of businesses have a similar opportunity in leveraging data mining techniques for improving their respective processes and ultimately, economic returns. Reducing expenses and maximizing profits is a shared challenge among for-profit institutions. As a result, it is not uncommon for corporations spanning various disciplines to use mined data in conjunction with analytic and predictive methods for getting a more informed business perspective. Ethically, many of the issues that confront businesses with respect to data mining are similar to other contextual use cases: they revolve around the notion of privacy and the extent to which the resulting information can be shared with third-parties. Because the benefits of such activity can be used to “...reduce fraud, anticipate resource demand, increase acquisition and curb customer attrition,” the attractive nature of the process generally outweighs any lingering moral uncertainties.<ref>SAS Institute. (2003). SAS data mining tools drive growing segment of business intelligence market [Press release]. Retrieved from http://www.itweb.co.za/office/sas/PressRelease.php?StoryID=135244</ref><br />
<br />
===Use Case #3: Government===<br />
Privacy is a theme that pertains to almost every possible application of data mining as a mechanism for revealing information. This happens to be no different when discussed in the framework of government utilization. There are hundreds of available uses, with some of the most recently relevant being in the realm of predicting and and preventing acts of terrorism. The broad and sweeping nature of data mining, especially in the pursuit of foreign national intelligence acquisition, can have unintended effects despite its many arguably useful implementations for combating terror and crimes against humanity. Among others issues, this exceptionally thorough process has been proven to threaten the privacy of United States citizens who, without an otherwise legitimate underlying connection, were mined and found to falsely possess direct ties to criminal or extremist organizations. Contingently included in the mining as a result of accidental or indirect associations, the incorrect isolation and thus the practice itself -- predicated on a more is better than less mentality -- can have dangerous repercussions when not safely managed and implemented.<ref>http://www.govexec.com/defense/2005/12/army-project-illustrates-promise-shortcomings-of-data-mining/20758/</ref> Furthermore, the construction of possibly erroneous correlative models built from the massive accumulation of mined data may, in some circumstances, display a statistically significant relationship where one does not actually exist. A pitfall of data analysis on a super large sample is that nearly any difference in quantitative measure, even minor, has the potential to reflect a casual association without a realistic or practical underpinning.<ref>Helberg, Clay (1996). Pitfalls of data analysis. Practical Assessment, Research & Evaluation, 5(5). Retrieved April 21, 2016 from http://PAREonline.net/getvn.asp?v=5&n=5</ref> The potential for flawed outcomes must be taken into active consideration when drawing conclusions from comprehensive data mining activities and analysis.<br />
<br />
===Use Case #4: Healthcare===<br />
As more information related to individuals’ health records becomes digitized, data mining will become significantly more useful within the healthcare field. Patients’ health records, when used in aggregate, will provide significant insight into the causes of specific illnesses. The success of data mining in this context hinges on the process of converting significant amounts of data into standardized digital formats on software systems with low storage and search costs.<br />
<br />
==See also==<br />
* [[Data Aggregation Online]]<br />
* [[Targeted Advertising (Online)]]<br />
* [[Geographic Information Systems]]<br />
*[[Recommender Systems]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Concepts]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Data_Mining&diff=56938Data Mining2016-04-21T18:44:48Z<p>Zzasuwa: /* Use Case #3: Government */</p>
<hr />
<div>[[File:DiagramDataMining.jpg|right|thumb|350px|This illustration shows the role [[Wikipedia:Data Mining|Data Mining]] plays in processing information for business use.]]<br />
'''Data Mining''' is the act of analyzing data from various perspectives and summarizing it into useful information, which combines aspects of artificial intelligence, machine learning, statistics and database systems. Software is implemented as one of many analytical tools used to analyze data. Through data mining, data is presented to users from many different angles, in various categories and relationships. On a more technical term, data mining is the process of realizing correlations or patterns among large fields of relative databases.<ref name="palace">[http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/index.htm Palace, Bill. "What Is Data Mining?" Data Mining. Anderson Graduate School of Management at UCLA, Mar. 1996. Web. 16 Dec. 2011.]</ref><br />
<br />
Data mining is used in many fields such as business, marketing, engineering, medicine, and the music and gaming industry. Businesses employ data mining techniques to gather information on their customers to better advertise and target future customers, while data mining is used to recognize patterns and designate categories for work in gaming, medicine, and music. Because personal information is gathered and studied, ethical concerns arise from the lack of anonymity and privacy of the subjects used in data mining techniques.<br />
<br />
<br />
==Process of Data Mining==<br />
The process of Data Mining requires the extraction of useful information from large sets of data stored in a uniform way, often in a database. The type of information extracted is relative to the type of data available, and the purpose of the data mining project itself – the information extracted could be modeled or represented of the entire set of data for example if the objective was to draw a conclusion of the dataset as a whole. <ref>http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf</ref><br />
<br />
Data mining consists of multiple sub-tasks:<ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Olaru, C., & Wehenkel, L. (1999). Data mining. IEEE Computer Applications in Power, 12(3), 19-25. doi:10.1109/67.773801]</ref><ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Shen Bin, Liu Yuan, & Wang Xiaoyi. (2010). Research on data mining models for the internet of things. 2010 International Conference on Image Analysis and Signal Processing (IASP) (pp. 127-132). Presented at the 2010 International Conference on Image Analysis and Signal Processing (IASP), IEEE. doi:10.1109/IASP.2010.5476146]</ref><br />
<br />
===Anomaly Detection=== <br />
Also called deviation detection, this is the “data cleaning” process where data miners attempt to identify unusual circumstances within the data that may belie anomalies or errors. The process includes program-aided searches of the data, parsing the data through filters that check for certain cases, and searching for unexplainable patterns within the data. The anomaly detection process often requires checking how the data was aggregated, and if human error had an effect on the data present.<br />
<br />
===Clustering=== <br />
[[File:Clustered.png|right|thumb|350px|A database of songs that has been visually represented as clusters of data which best represent the categories of songs present (ie. hip-hop, punk).]]<br />
Situations arise within datasets where there is a desire to know if similarities exist within the data, but no mechanism for detecting them exists. Clustering procedures within data mining aim to find similar objects within the data or to analyze correlations between attributes of the data. Clustering is also used to find topographical similarities within data sets.<br />
<br />
===Classification=== <br />
Use of mined data requires the input and output information to be formatted in a discrete way in order for the information to be useful. Classifying traits of both the input and output of the data minimizes ambiguity, and helps define unique qualities of the data, and the meaning of the data itself. This process also helps discover unique relationships that exist between the data present in the database (the input data), and information that is being presented as conclusive of the data set (the output data). Classification can occur in many formats, from labeling, to building models which fit the particular data set explicitly.<br />
<br />
===Regression=== <br />
The building of a transparent model which fits the data, and adequately conveys the information contained within the data set is the objective of the regression step of data mining. The use of statistical methods is the most common approach within the regression step, because it yields discrete output. Regression modeling also attempts to minimize error within the other steps of the data mining process.<br />
<br />
===Summarization===<br />
This task aims at producing representative descriptions of the data which provides context for the data. The summary process can take multiple formats, the most common of which is numerical analysis, which provides statistical quantification for patterns in the data like the mean or standard deviation found within the data set. These results are often represented in graphical formats – such as histograms or scatter plots. Qualitative data can be summarized by giving a list of trends or frequencies within a set of data. These results are useful because they represent the entirety of the data set in a much more digestible format.<br />
<br />
==Data Mining Tools==<br />
<br />
As data mining is becoming more popular to sift through the massive amounts of data available online, new tools have been cropping up to make the job simpler. Google, for example, has released a free tool called Correlate<ref>http://www.fastcompany.com/1755287/google-correlate-tool-gives-marketers-powerful-new-data-mining-tools</ref> aimed at helping corporations utilize the Google search database to draw conclusions on consumer behavior. MIT and Harvard have recently collaborated in the creation of MINE, a tool of unprecedented power which can analyze more data for more complex patterns than any tool previously available. It is said to approach the quality of human examination in terms of being able to pick up on nonstandard or deceptive patterns<ref>http://www.decodedscience.com/data-mining-tool-advances-mine-ranks-multiple-patterns/7959</ref>.<br />
<br />
==Applications of Data Mining==<br />
===Examples===<br />
A simple example of data mining is analyzing a large population, such as University of Michigan students, and determining simple characteristics that the data has, such as the proportion of the student body that is from each ethnic background.<br />
<br />
Before the term "data mining" came into popular use, many businesses had already implemented its technology. They used powerful computers to comb through quantitative data from supermarket scanners, and analyzed the resulting data for market research purposes. This process have been immensely increasing the precision of analysis, and at the same time decreasing the cost of research.<ref name="palace"/><br />
<br />
===Business===<br />
Data mining is commonly used in business <ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:bookitem&rft.title=Introduction+to+Data+Mining+and+its+Applications&rft.atitle=Data+Mining+in+Sales+Marketing+and+Finance&rft.creator=Sumathi%2C+S&rft.creator=Sivanandam%2C+S+N&rft.series=Studies+in+Computational+Intelligence&rft.date=2006-01-01&rft.pub=Springer+Berlin+Heidelberg&rft.isbn=9783540343509&rft.issn=1860-949X&rft.volume=29&rft.spage=411&rft.epage=438&rft_id=info:doi/10.1007%2F978-3-540-34351-6_18&rft.externalDBID=n%2Fa&rft.externalDocID=978-3-540-34351-6_133505_Chap18 Data Mining in Sales Marketing and Finance. (2006). Introduction to Data Mining and its Applications (Vol. 29, pp. 411-438). Berlin, Heidelberg: Springer Berlin Heidelberg.]</ref> to determine what demographics are buying what products, and to help firms predict customer preferences. Online retailers often track the ways which their clients interact with the content on their site. This leads to an extensive collection of data that can be used by data mining procedures to model representative "personas" of future clients who will visit the site. This has meaningful value for companies, because information generated from data mining practices can lead to more "click-throughs" and potentially, more "conversions" (sales).<br />
<br />
===Science/Academics===<br />
Data Mining also plays a crucial role in scientific analysis. The objective of many large research studies is to find patterns in experimental data. Many data mining procedures allow for applicable modeling of data in a way that is much faster and easier than doing so by hand.<br />
<br />
===Other Uses===<br />
Data Mining techniques have been used in a variety of other matters, such as in admissions processes in universities and political campaigns. <ref>http://communities.washingtontimes.com/neighborhood/political-potpourri/2012/nov/17/how-obama-won/</ref><br />
<ref>http://www.tnr.com/article/110862/data-mining-the-ineffective-future-affirmative-action-in-education#</ref> <br />
<br />
==Ethical Implications==<br />
Data mining is an inherently morally-neutral action, as it is simply the practice of working with large stored data sets and the process itself does not account for the way that the data was generated or will inevitably be used. The ethics of data mining come under scrutiny when the type of data being mined is something that the individuals or specific entities subject to the mining feel should not be disclosed for others to view. Issues also arise when sufficiently robust restrictions are not in place to hide the private characteristics of an individual’s personal identity in the online environment. Likewise, when firms neglect to take adequately tested precautionary measures for protecting a customer’s data as part of a larger collective set of consumer information, they are effectively opening all their clients’ data up to the possibility of unprotected digital consumption and exploitation.<br />
<br />
===User Profiling and Identification===<br />
In an attempt to build accurate models, data miners in online environments occasionally utilize the personal information of individuals who visit their sites. The information generally collected includes, but is not limited to, geographic identifiers, records of past behavior, or information that can identify an individual by a range of other intimate attributes like user account details or IP addresses. Many users do not actively realize that their information is being collected and many question the ethics of using an individual’s private information without their outright and formal consent.<ref>http://www.ecommercetimes.com/story/52616.html</ref> Additionally, data mining practices and tools have made the automated profiling of groups and individuals an easy task for a growing number of the technically literate. Using data mining to create customer profiling questions also raises ethical concerns regarding these practices due to the associated risks it creates for discrimination, de-individualization, and information asymmetries.<ref>Schermer, B.W. (2011). The limits of privacy in automated profiling and data mining. Computer Law and Security Report, 27(1), pp. 45-52.</ref><br />
<br />
===Distortion of Truth===<br />
Second to privacy concerns, distorting or misrepresenting trends in data that are not actually there together account for the other main ethical issue on the topic of data mining. The result of human or programmatic mishaps, generating flawed associations between data points can be severely costly to reputations, economic footings, and the continued confidence in the reliability of the data analysis method.<br />
<br />
===Anonymity and Ownership===<br />
Many information systems contain data on individuals, so maintaining anonymity of the data and informing those whose information is being used, exactly how it is used and to what effect, is not only important but in many cases a legally binding matter. Allowing individuals the freedom to opt in or opt out of data mining processes is one way to ensure some form of ethical responsibility.<br />
<br />
Discussion regarding the role of data ownership emerges as a natural consequence to its collection from disparate sources and its subsequent composite processing, analysis, and summarization. A contested point in some circumstances is the differentiation between raw source data (input) and the proprietary information product (output). Because the former, when in aggregate form, represents the constituent building blocks of the latter, the resulting interdependency spawns many debates surrounding who, if anyone, rightfully owns each intermediate information good and the finished analysis. Boiling down to where lines are drawn with respect to voluntary (or involuntary) data relinquishment and the perception of residual value, the ground is ripe for dispute and ethical ambiguity.<br />
<br />
===Use Case #1: Social Networking===<br />
Mining data found on social networking sites poses a particular challenge, as a great deal of personal information can be found on individuals’ social media pages. It is not uncommon for social media firms like Facebook and Twitter to sell the information mined from users’ personal pages to advertisers and academic researchers. Despite the fact that this data is anonymized and/or collected from a large pool of public-facing accounts, there are still a number of valid sources for apprehension regarding its execution and overall legitimacy.<br />
<br />
To reduce users’ qualms surrounding this type of activity and to ensure that the ethical validity of all such experimentation meets a sound moral standard, committees in the form of [[Wikipedia:Institutional_review_board|institutional review boards]] (IRBs) are organized across both industry and the social science disciplines of higher-learning. They are entrusted with the responsibility for reviewing, monitoring, and approving of any biomedical or behavioral research involving humans.<ref>Wikipedia: Institutional review board https://en.wikipedia.org/wiki/Institutional_review_board</ref> Because the natural extension of examining humans’ behavioral tendencies places us in the digital realm and thus satisfies the need for the aforementioned requisite oversight, here too IRBs serve a vital role in fulfilling the third-party ethical assessment of data collection and data manipulation for the sake of research driven advertising or academic inquiry.<br />
<br />
Firms often explicitly state in the terms and conditions of using their sites that they reserve the right to sell or use an individual's personally volunteered information toward serving their own business needs. Although this activity is controlled and kept within defined limits by consumer privacy laws, vigilant monitoring is necessary by the [[Wikipedia:Federal_Trade_Commission|Federal Trade Commission]] (FTC) and other such organizations to ensure that businesses are conducting themselves both fairly and lawfully. Because a user agreement is usually only displayed once (typically during the registration process), if it all, the gravity and seriousness ascribed to its contents is frequently undervalued by users, often relegated as unimportant and a low priority.<br />
<br />
As a majority of avid social media users are only concerned about a platform’s (superficially free) software and community assets -- expressed through its ability to cultivate social capital or the manner by which it advocates for a “more open and connected world” (e.g. Facebook) -- the embedded costs posed by data mining to users are subliminal and certainly not without their ramifications.<ref>Facebook, [http://www.facebook.com/advertising/ Advertising]</ref> Many users feel that the pursuit is an irresponsible practice, irrespective of the safeguards and stipulations found in user agreements, and consider it to be an activity in which social media firms should not engage.<ref>http://mashable.com/2011/02/25/data-mining-social-marketing/</ref> Often for that reason, or purely on the rationale that the sum total of one’s social contributions could be aggregated and packaged up for sale at any time to an outside party -- without due notification or option for opting out -- many people who choose not to participate on Web-based social platforms due so for concerns that mirror those convictions. Increasing the transparency of what data is mined and identifying when a user is particularly vulnerable (i.e. for disregarding privacy measures) has the potential to restore the necessary confidence in weary users.<br />
<br />
===Use Case #2: Business===<br />
Much like the value that can be derived from mining social sites, a wide array of businesses have a similar opportunity in leveraging data mining techniques for improving their respective processes and ultimately, economic returns. Reducing expenses and maximizing profits is a shared challenge among for-profit institutions. As a result, it is not uncommon for corporations spanning various disciplines to use mined data in conjunction with analytic and predictive methods for getting a more informed business perspective. Ethically, many of the issues that confront businesses with respect to data mining are similar to other contextual use cases: they revolve around the notion of privacy and the extent to which the resulting information can be shared with third-parties. Because the benefits of such activity can be used to “...reduce fraud, anticipate resource demand, increase acquisition and curb customer attrition,” the attractive nature of the process generally outweighs any lingering moral uncertainties.<ref>SAS Institute. (2003). SAS data mining tools drive growing segment of business intelligence market [Press release]. Retrieved from http://www.itweb.co.za/office/sas/PressRelease.php?StoryID=135244</ref><br />
<br />
===Use Case #3: Government===<br />
Privacy is a theme that pertains to almost every possible application of data mining as a mechanism for revealing information. This happens to be no different when discussed in the framework of government utilization. There are hundreds of available uses, with some of the most recently relevant being in the realm of predicting and and preventing acts of terrorism. The broad and sweeping nature of data mining, especially in the pursuit of foreign national intelligence acquisition, can have unintended effects despite its many arguably useful implementations. Among others issues, this exceptionally thorough process has been proven to threaten the privacy of United States citizens who, without an otherwise legitimate underlying connection, were found to possess no direct ties to extremist organizations and were contingently included in the mining as a result of accidental or indirect associations.<ref>http://www.govexec.com/defense/2005/12/army-project-illustrates-promise-shortcomings-of-data-mining/20758/</ref> Furthermore, the construction of possibly erroneous correlative models built from the massive accumulation of mined data may, in some circumstances, display a statistically significant relationship where one does not actually exist. A pitfall of data analysis on a super large sample is that nearly any difference in quantitative measure, even minor, has the potential to reflect a casual association without a realistic or practical underpinning.<ref>Helberg, Clay (1996). Pitfalls of data analysis. Practical Assessment, Research & Evaluation, 5(5). Retrieved April 21, 2016 from http://PAREonline.net/getvn.asp?v=5&n=5</ref> The potential for flawed outcomes must be taken into active consideration when drawing conclusions from comprehensive data mining activities and analysis.<br />
<br />
===Use Case #4: Healthcare===<br />
As more information related to individuals’ health records becomes digitized, data mining will become significantly more useful within the healthcare field. Patients’ health records, when used in aggregate, will provide significant insight into the causes of specific illnesses. The success of data mining in this context hinges on the process of converting significant amounts of data into standardized digital formats on software systems with low storage and search costs.<br />
<br />
==See also==<br />
* [[Data Aggregation Online]]<br />
* [[Targeted Advertising (Online)]]<br />
* [[Geographic Information Systems]]<br />
*[[Recommender Systems]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Concepts]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Data_Mining&diff=56935Data Mining2016-04-21T17:22:12Z<p>Zzasuwa: /* Use Case #2: Business */</p>
<hr />
<div>[[File:DiagramDataMining.jpg|right|thumb|350px|This illustration shows the role [[Wikipedia:Data Mining|Data Mining]] plays in processing information for business use.]]<br />
'''Data Mining''' is the act of analyzing data from various perspectives and summarizing it into useful information, which combines aspects of artificial intelligence, machine learning, statistics and database systems. Software is implemented as one of many analytical tools used to analyze data. Through data mining, data is presented to users from many different angles, in various categories and relationships. On a more technical term, data mining is the process of realizing correlations or patterns among large fields of relative databases.<ref name="palace">[http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/index.htm Palace, Bill. "What Is Data Mining?" Data Mining. Anderson Graduate School of Management at UCLA, Mar. 1996. Web. 16 Dec. 2011.]</ref><br />
<br />
Data mining is used in many fields such as business, marketing, engineering, medicine, and the music and gaming industry. Businesses employ data mining techniques to gather information on their customers to better advertise and target future customers, while data mining is used to recognize patterns and designate categories for work in gaming, medicine, and music. Because personal information is gathered and studied, ethical concerns arise from the lack of anonymity and privacy of the subjects used in data mining techniques.<br />
<br />
<br />
==Process of Data Mining==<br />
The process of Data Mining requires the extraction of useful information from large sets of data stored in a uniform way, often in a database. The type of information extracted is relative to the type of data available, and the purpose of the data mining project itself – the information extracted could be modeled or represented of the entire set of data for example if the objective was to draw a conclusion of the dataset as a whole. <ref>http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf</ref><br />
<br />
Data mining consists of multiple sub-tasks:<ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Olaru, C., & Wehenkel, L. (1999). Data mining. IEEE Computer Applications in Power, 12(3), 19-25. doi:10.1109/67.773801]</ref><ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Shen Bin, Liu Yuan, & Wang Xiaoyi. (2010). Research on data mining models for the internet of things. 2010 International Conference on Image Analysis and Signal Processing (IASP) (pp. 127-132). Presented at the 2010 International Conference on Image Analysis and Signal Processing (IASP), IEEE. doi:10.1109/IASP.2010.5476146]</ref><br />
<br />
===Anomaly Detection=== <br />
Also called deviation detection, this is the “data cleaning” process where data miners attempt to identify unusual circumstances within the data that may belie anomalies or errors. The process includes program-aided searches of the data, parsing the data through filters that check for certain cases, and searching for unexplainable patterns within the data. The anomaly detection process often requires checking how the data was aggregated, and if human error had an effect on the data present.<br />
<br />
===Clustering=== <br />
[[File:Clustered.png|right|thumb|350px|A database of songs that has been visually represented as clusters of data which best represent the categories of songs present (ie. hip-hop, punk).]]<br />
Situations arise within datasets where there is a desire to know if similarities exist within the data, but no mechanism for detecting them exists. Clustering procedures within data mining aim to find similar objects within the data or to analyze correlations between attributes of the data. Clustering is also used to find topographical similarities within data sets.<br />
<br />
===Classification=== <br />
Use of mined data requires the input and output information to be formatted in a discrete way in order for the information to be useful. Classifying traits of both the input and output of the data minimizes ambiguity, and helps define unique qualities of the data, and the meaning of the data itself. This process also helps discover unique relationships that exist between the data present in the database (the input data), and information that is being presented as conclusive of the data set (the output data). Classification can occur in many formats, from labeling, to building models which fit the particular data set explicitly.<br />
<br />
===Regression=== <br />
The building of a transparent model which fits the data, and adequately conveys the information contained within the data set is the objective of the regression step of data mining. The use of statistical methods is the most common approach within the regression step, because it yields discrete output. Regression modeling also attempts to minimize error within the other steps of the data mining process.<br />
<br />
===Summarization===<br />
This task aims at producing representative descriptions of the data which provides context for the data. The summary process can take multiple formats, the most common of which is numerical analysis, which provides statistical quantification for patterns in the data like the mean or standard deviation found within the data set. These results are often represented in graphical formats – such as histograms or scatter plots. Qualitative data can be summarized by giving a list of trends or frequencies within a set of data. These results are useful because they represent the entirety of the data set in a much more digestible format.<br />
<br />
==Data Mining Tools==<br />
<br />
As data mining is becoming more popular to sift through the massive amounts of data available online, new tools have been cropping up to make the job simpler. Google, for example, has released a free tool called Correlate<ref>http://www.fastcompany.com/1755287/google-correlate-tool-gives-marketers-powerful-new-data-mining-tools</ref> aimed at helping corporations utilize the Google search database to draw conclusions on consumer behavior. MIT and Harvard have recently collaborated in the creation of MINE, a tool of unprecedented power which can analyze more data for more complex patterns than any tool previously available. It is said to approach the quality of human examination in terms of being able to pick up on nonstandard or deceptive patterns<ref>http://www.decodedscience.com/data-mining-tool-advances-mine-ranks-multiple-patterns/7959</ref>.<br />
<br />
==Applications of Data Mining==<br />
===Examples===<br />
A simple example of data mining is analyzing a large population, such as University of Michigan students, and determining simple characteristics that the data has, such as the proportion of the student body that is from each ethnic background.<br />
<br />
Before the term "data mining" came into popular use, many businesses had already implemented its technology. They used powerful computers to comb through quantitative data from supermarket scanners, and analyzed the resulting data for market research purposes. This process have been immensely increasing the precision of analysis, and at the same time decreasing the cost of research.<ref name="palace"/><br />
<br />
===Business===<br />
Data mining is commonly used in business <ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:bookitem&rft.title=Introduction+to+Data+Mining+and+its+Applications&rft.atitle=Data+Mining+in+Sales+Marketing+and+Finance&rft.creator=Sumathi%2C+S&rft.creator=Sivanandam%2C+S+N&rft.series=Studies+in+Computational+Intelligence&rft.date=2006-01-01&rft.pub=Springer+Berlin+Heidelberg&rft.isbn=9783540343509&rft.issn=1860-949X&rft.volume=29&rft.spage=411&rft.epage=438&rft_id=info:doi/10.1007%2F978-3-540-34351-6_18&rft.externalDBID=n%2Fa&rft.externalDocID=978-3-540-34351-6_133505_Chap18 Data Mining in Sales Marketing and Finance. (2006). Introduction to Data Mining and its Applications (Vol. 29, pp. 411-438). Berlin, Heidelberg: Springer Berlin Heidelberg.]</ref> to determine what demographics are buying what products, and to help firms predict customer preferences. Online retailers often track the ways which their clients interact with the content on their site. This leads to an extensive collection of data that can be used by data mining procedures to model representative "personas" of future clients who will visit the site. This has meaningful value for companies, because information generated from data mining practices can lead to more "click-throughs" and potentially, more "conversions" (sales).<br />
<br />
===Science/Academics===<br />
Data Mining also plays a crucial role in scientific analysis. The objective of many large research studies is to find patterns in experimental data. Many data mining procedures allow for applicable modeling of data in a way that is much faster and easier than doing so by hand.<br />
<br />
===Other Uses===<br />
Data Mining techniques have been used in a variety of other matters, such as in admissions processes in universities and political campaigns. <ref>http://communities.washingtontimes.com/neighborhood/political-potpourri/2012/nov/17/how-obama-won/</ref><br />
<ref>http://www.tnr.com/article/110862/data-mining-the-ineffective-future-affirmative-action-in-education#</ref> <br />
<br />
==Ethical Implications==<br />
Data mining is an inherently morally-neutral action, as it is simply the practice of working with large stored data sets and the process itself does not account for the way that the data was generated or will inevitably be used. The ethics of data mining come under scrutiny when the type of data being mined is something that the individuals or specific entities subject to the mining feel should not be disclosed for others to view. Issues also arise when sufficiently robust restrictions are not in place to hide the private characteristics of an individual’s personal identity in the online environment. Likewise, when firms neglect to take adequately tested precautionary measures for protecting a customer’s data as part of a larger collective set of consumer information, they are effectively opening all their clients’ data up to the possibility of unprotected digital consumption and exploitation.<br />
<br />
===User Profiling and Identification===<br />
In an attempt to build accurate models, data miners in online environments occasionally utilize the personal information of individuals who visit their sites. The information generally collected includes, but is not limited to, geographic identifiers, records of past behavior, or information that can identify an individual by a range of other intimate attributes like user account details or IP addresses. Many users do not actively realize that their information is being collected and many question the ethics of using an individual’s private information without their outright and formal consent.<ref>http://www.ecommercetimes.com/story/52616.html</ref> Additionally, data mining practices and tools have made the automated profiling of groups and individuals an easy task for a growing number of the technically literate. Using data mining to create customer profiling questions also raises ethical concerns regarding these practices due to the associated risks it creates for discrimination, de-individualization, and information asymmetries.<ref>Schermer, B.W. (2011). The limits of privacy in automated profiling and data mining. Computer Law and Security Report, 27(1), pp. 45-52.</ref><br />
<br />
===Distortion of Truth===<br />
Second to privacy concerns, distorting or misrepresenting trends in data that are not actually there together account for the other main ethical issue on the topic of data mining. The result of human or programmatic mishaps, generating flawed associations between data points can be severely costly to reputations, economic footings, and the continued confidence in the reliability of the data analysis method.<br />
<br />
===Anonymity and Ownership===<br />
Many information systems contain data on individuals, so maintaining anonymity of the data and informing those whose information is being used, exactly how it is used and to what effect, is not only important but in many cases a legally binding matter. Allowing individuals the freedom to opt in or opt out of data mining processes is one way to ensure some form of ethical responsibility.<br />
<br />
Discussion regarding the role of data ownership emerges as a natural consequence to its collection from disparate sources and its subsequent composite processing, analysis, and summarization. A contested point in some circumstances is the differentiation between raw source data (input) and the proprietary information product (output). Because the former, when in aggregate form, represents the constituent building blocks of the latter, the resulting interdependency spawns many debates surrounding who, if anyone, rightfully owns each intermediate information good and the finished analysis. Boiling down to where lines are drawn with respect to voluntary (or involuntary) data relinquishment and the perception of residual value, the ground is ripe for dispute and ethical ambiguity.<br />
<br />
===Use Case #1: Social Networking===<br />
Mining data found on social networking sites poses a particular challenge, as a great deal of personal information can be found on individuals’ social media pages. It is not uncommon for social media firms like Facebook and Twitter to sell the information mined from users’ personal pages to advertisers and academic researchers. Despite the fact that this data is anonymized and/or collected from a large pool of public-facing accounts, there are still a number of valid sources for apprehension regarding its execution and overall legitimacy.<br />
<br />
To reduce users’ qualms surrounding this type of activity and to ensure that the ethical validity of all such experimentation meets a sound moral standard, committees in the form of [[Wikipedia:Institutional_review_board|institutional review boards]] (IRBs) are organized across both industry and the social science disciplines of higher-learning. They are entrusted with the responsibility for reviewing, monitoring, and approving of any biomedical or behavioral research involving humans.<ref>Wikipedia: Institutional review board https://en.wikipedia.org/wiki/Institutional_review_board</ref> Because the natural extension of examining humans’ behavioral tendencies places us in the digital realm and thus satisfies the need for the aforementioned requisite oversight, here too IRBs serve a vital role in fulfilling the third-party ethical assessment of data collection and data manipulation for the sake of research driven advertising or academic inquiry.<br />
<br />
Firms often explicitly state in the terms and conditions of using their sites that they reserve the right to sell or use an individual's personally volunteered information toward serving their own business needs. Although this activity is controlled and kept within defined limits by consumer privacy laws, vigilant monitoring is necessary by the [[Wikipedia:Federal_Trade_Commission|Federal Trade Commission]] (FTC) and other such organizations to ensure that businesses are conducting themselves both fairly and lawfully. Because a user agreement is usually only displayed once (typically during the registration process), if it all, the gravity and seriousness ascribed to its contents is frequently undervalued by users, often relegated as unimportant and a low priority.<br />
<br />
As a majority of avid social media users are only concerned about a platform’s (superficially free) software and community assets -- expressed through its ability to cultivate social capital or the manner by which it advocates for a “more open and connected world” (e.g. Facebook) -- the embedded costs posed by data mining to users are subliminal and certainly not without their ramifications.<ref>Facebook, [http://www.facebook.com/advertising/ Advertising]</ref> Many users feel that the pursuit is an irresponsible practice, irrespective of the safeguards and stipulations found in user agreements, and consider it to be an activity in which social media firms should not engage.<ref>http://mashable.com/2011/02/25/data-mining-social-marketing/</ref> Often for that reason, or purely on the rationale that the sum total of one’s social contributions could be aggregated and packaged up for sale at any time to an outside party -- without due notification or option for opting out -- many people who choose not to participate on Web-based social platforms due so for concerns that mirror those convictions. Increasing the transparency of what data is mined and identifying when a user is particularly vulnerable (i.e. for disregarding privacy measures) has the potential to restore the necessary confidence in weary users.<br />
<br />
===Use Case #2: Business===<br />
Much like the value that can be derived from mining social sites, a wide array of businesses have a similar opportunity in leveraging data mining techniques for improving their respective processes and ultimately, economic returns. Reducing expenses and maximizing profits is a shared challenge among for-profit institutions. As a result, it is not uncommon for corporations spanning various disciplines to use mined data in conjunction with analytic and predictive methods for getting a more informed business perspective. Ethically, many of the issues that confront businesses with respect to data mining are similar to other contextual use cases: they revolve around the notion of privacy and the extent to which the resulting information can be shared with third-parties. Because the benefits of such activity can be used to “...reduce fraud, anticipate resource demand, increase acquisition and curb customer attrition,” the attractive nature of the process generally outweighs any lingering moral uncertainties.<ref>SAS Institute. (2003). SAS data mining tools drive growing segment of business intelligence market [Press release]. Retrieved from http://www.itweb.co.za/office/sas/PressRelease.php?StoryID=135244</ref><br />
<br />
===Use Case #3: Government===<br />
Privacy is a theme that pertains to almost every possible application of data mining as a mechanism for revealing information. This happens to be no different when discussed in the framework of government utilization. There are hundreds of available uses, with some of the most recently relevant being in the realm of predicting and and preventing acts of terrorism.<br />
<br />
===Use Case #4: Healthcare===<br />
As more information related to individuals’ health records becomes digitized, data mining will become significantly more useful within the healthcare field. Patients’ health records, when used in aggregate, will provide significant insight into the causes of specific illnesses. The success of data mining in this context hinges on the process of converting significant amounts of data into standardized digital formats on software systems with low storage and search costs.<br />
<br />
==See also==<br />
* [[Data Aggregation Online]]<br />
* [[Targeted Advertising (Online)]]<br />
* [[Geographic Information Systems]]<br />
*[[Recommender Systems]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Concepts]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Data_Mining&diff=56934Data Mining2016-04-21T17:10:40Z<p>Zzasuwa: /* Use Case #1: Social Networking */</p>
<hr />
<div>[[File:DiagramDataMining.jpg|right|thumb|350px|This illustration shows the role [[Wikipedia:Data Mining|Data Mining]] plays in processing information for business use.]]<br />
'''Data Mining''' is the act of analyzing data from various perspectives and summarizing it into useful information, which combines aspects of artificial intelligence, machine learning, statistics and database systems. Software is implemented as one of many analytical tools used to analyze data. Through data mining, data is presented to users from many different angles, in various categories and relationships. On a more technical term, data mining is the process of realizing correlations or patterns among large fields of relative databases.<ref name="palace">[http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/index.htm Palace, Bill. "What Is Data Mining?" Data Mining. Anderson Graduate School of Management at UCLA, Mar. 1996. Web. 16 Dec. 2011.]</ref><br />
<br />
Data mining is used in many fields such as business, marketing, engineering, medicine, and the music and gaming industry. Businesses employ data mining techniques to gather information on their customers to better advertise and target future customers, while data mining is used to recognize patterns and designate categories for work in gaming, medicine, and music. Because personal information is gathered and studied, ethical concerns arise from the lack of anonymity and privacy of the subjects used in data mining techniques.<br />
<br />
<br />
==Process of Data Mining==<br />
The process of Data Mining requires the extraction of useful information from large sets of data stored in a uniform way, often in a database. The type of information extracted is relative to the type of data available, and the purpose of the data mining project itself – the information extracted could be modeled or represented of the entire set of data for example if the objective was to draw a conclusion of the dataset as a whole. <ref>http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf</ref><br />
<br />
Data mining consists of multiple sub-tasks:<ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Olaru, C., & Wehenkel, L. (1999). Data mining. IEEE Computer Applications in Power, 12(3), 19-25. doi:10.1109/67.773801]</ref><ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Shen Bin, Liu Yuan, & Wang Xiaoyi. (2010). Research on data mining models for the internet of things. 2010 International Conference on Image Analysis and Signal Processing (IASP) (pp. 127-132). Presented at the 2010 International Conference on Image Analysis and Signal Processing (IASP), IEEE. doi:10.1109/IASP.2010.5476146]</ref><br />
<br />
===Anomaly Detection=== <br />
Also called deviation detection, this is the “data cleaning” process where data miners attempt to identify unusual circumstances within the data that may belie anomalies or errors. The process includes program-aided searches of the data, parsing the data through filters that check for certain cases, and searching for unexplainable patterns within the data. The anomaly detection process often requires checking how the data was aggregated, and if human error had an effect on the data present.<br />
<br />
===Clustering=== <br />
[[File:Clustered.png|right|thumb|350px|A database of songs that has been visually represented as clusters of data which best represent the categories of songs present (ie. hip-hop, punk).]]<br />
Situations arise within datasets where there is a desire to know if similarities exist within the data, but no mechanism for detecting them exists. Clustering procedures within data mining aim to find similar objects within the data or to analyze correlations between attributes of the data. Clustering is also used to find topographical similarities within data sets.<br />
<br />
===Classification=== <br />
Use of mined data requires the input and output information to be formatted in a discrete way in order for the information to be useful. Classifying traits of both the input and output of the data minimizes ambiguity, and helps define unique qualities of the data, and the meaning of the data itself. This process also helps discover unique relationships that exist between the data present in the database (the input data), and information that is being presented as conclusive of the data set (the output data). Classification can occur in many formats, from labeling, to building models which fit the particular data set explicitly.<br />
<br />
===Regression=== <br />
The building of a transparent model which fits the data, and adequately conveys the information contained within the data set is the objective of the regression step of data mining. The use of statistical methods is the most common approach within the regression step, because it yields discrete output. Regression modeling also attempts to minimize error within the other steps of the data mining process.<br />
<br />
===Summarization===<br />
This task aims at producing representative descriptions of the data which provides context for the data. The summary process can take multiple formats, the most common of which is numerical analysis, which provides statistical quantification for patterns in the data like the mean or standard deviation found within the data set. These results are often represented in graphical formats – such as histograms or scatter plots. Qualitative data can be summarized by giving a list of trends or frequencies within a set of data. These results are useful because they represent the entirety of the data set in a much more digestible format.<br />
<br />
==Data Mining Tools==<br />
<br />
As data mining is becoming more popular to sift through the massive amounts of data available online, new tools have been cropping up to make the job simpler. Google, for example, has released a free tool called Correlate<ref>http://www.fastcompany.com/1755287/google-correlate-tool-gives-marketers-powerful-new-data-mining-tools</ref> aimed at helping corporations utilize the Google search database to draw conclusions on consumer behavior. MIT and Harvard have recently collaborated in the creation of MINE, a tool of unprecedented power which can analyze more data for more complex patterns than any tool previously available. It is said to approach the quality of human examination in terms of being able to pick up on nonstandard or deceptive patterns<ref>http://www.decodedscience.com/data-mining-tool-advances-mine-ranks-multiple-patterns/7959</ref>.<br />
<br />
==Applications of Data Mining==<br />
===Examples===<br />
A simple example of data mining is analyzing a large population, such as University of Michigan students, and determining simple characteristics that the data has, such as the proportion of the student body that is from each ethnic background.<br />
<br />
Before the term "data mining" came into popular use, many businesses had already implemented its technology. They used powerful computers to comb through quantitative data from supermarket scanners, and analyzed the resulting data for market research purposes. This process have been immensely increasing the precision of analysis, and at the same time decreasing the cost of research.<ref name="palace"/><br />
<br />
===Business===<br />
Data mining is commonly used in business <ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:bookitem&rft.title=Introduction+to+Data+Mining+and+its+Applications&rft.atitle=Data+Mining+in+Sales+Marketing+and+Finance&rft.creator=Sumathi%2C+S&rft.creator=Sivanandam%2C+S+N&rft.series=Studies+in+Computational+Intelligence&rft.date=2006-01-01&rft.pub=Springer+Berlin+Heidelberg&rft.isbn=9783540343509&rft.issn=1860-949X&rft.volume=29&rft.spage=411&rft.epage=438&rft_id=info:doi/10.1007%2F978-3-540-34351-6_18&rft.externalDBID=n%2Fa&rft.externalDocID=978-3-540-34351-6_133505_Chap18 Data Mining in Sales Marketing and Finance. (2006). Introduction to Data Mining and its Applications (Vol. 29, pp. 411-438). Berlin, Heidelberg: Springer Berlin Heidelberg.]</ref> to determine what demographics are buying what products, and to help firms predict customer preferences. Online retailers often track the ways which their clients interact with the content on their site. This leads to an extensive collection of data that can be used by data mining procedures to model representative "personas" of future clients who will visit the site. This has meaningful value for companies, because information generated from data mining practices can lead to more "click-throughs" and potentially, more "conversions" (sales).<br />
<br />
===Science/Academics===<br />
Data Mining also plays a crucial role in scientific analysis. The objective of many large research studies is to find patterns in experimental data. Many data mining procedures allow for applicable modeling of data in a way that is much faster and easier than doing so by hand.<br />
<br />
===Other Uses===<br />
Data Mining techniques have been used in a variety of other matters, such as in admissions processes in universities and political campaigns. <ref>http://communities.washingtontimes.com/neighborhood/political-potpourri/2012/nov/17/how-obama-won/</ref><br />
<ref>http://www.tnr.com/article/110862/data-mining-the-ineffective-future-affirmative-action-in-education#</ref> <br />
<br />
==Ethical Implications==<br />
Data mining is an inherently morally-neutral action, as it is simply the practice of working with large stored data sets and the process itself does not account for the way that the data was generated or will inevitably be used. The ethics of data mining come under scrutiny when the type of data being mined is something that the individuals or specific entities subject to the mining feel should not be disclosed for others to view. Issues also arise when sufficiently robust restrictions are not in place to hide the private characteristics of an individual’s personal identity in the online environment. Likewise, when firms neglect to take adequately tested precautionary measures for protecting a customer’s data as part of a larger collective set of consumer information, they are effectively opening all their clients’ data up to the possibility of unprotected digital consumption and exploitation.<br />
<br />
===User Profiling and Identification===<br />
In an attempt to build accurate models, data miners in online environments occasionally utilize the personal information of individuals who visit their sites. The information generally collected includes, but is not limited to, geographic identifiers, records of past behavior, or information that can identify an individual by a range of other intimate attributes like user account details or IP addresses. Many users do not actively realize that their information is being collected and many question the ethics of using an individual’s private information without their outright and formal consent.<ref>http://www.ecommercetimes.com/story/52616.html</ref> Additionally, data mining practices and tools have made the automated profiling of groups and individuals an easy task for a growing number of the technically literate. Using data mining to create customer profiling questions also raises ethical concerns regarding these practices due to the associated risks it creates for discrimination, de-individualization, and information asymmetries.<ref>Schermer, B.W. (2011). The limits of privacy in automated profiling and data mining. Computer Law and Security Report, 27(1), pp. 45-52.</ref><br />
<br />
===Distortion of Truth===<br />
Second to privacy concerns, distorting or misrepresenting trends in data that are not actually there together account for the other main ethical issue on the topic of data mining. The result of human or programmatic mishaps, generating flawed associations between data points can be severely costly to reputations, economic footings, and the continued confidence in the reliability of the data analysis method.<br />
<br />
===Anonymity and Ownership===<br />
Many information systems contain data on individuals, so maintaining anonymity of the data and informing those whose information is being used, exactly how it is used and to what effect, is not only important but in many cases a legally binding matter. Allowing individuals the freedom to opt in or opt out of data mining processes is one way to ensure some form of ethical responsibility.<br />
<br />
Discussion regarding the role of data ownership emerges as a natural consequence to its collection from disparate sources and its subsequent composite processing, analysis, and summarization. A contested point in some circumstances is the differentiation between raw source data (input) and the proprietary information product (output). Because the former, when in aggregate form, represents the constituent building blocks of the latter, the resulting interdependency spawns many debates surrounding who, if anyone, rightfully owns each intermediate information good and the finished analysis. Boiling down to where lines are drawn with respect to voluntary (or involuntary) data relinquishment and the perception of residual value, the ground is ripe for dispute and ethical ambiguity.<br />
<br />
===Use Case #1: Social Networking===<br />
Mining data found on social networking sites poses a particular challenge, as a great deal of personal information can be found on individuals’ social media pages. It is not uncommon for social media firms like Facebook and Twitter to sell the information mined from users’ personal pages to advertisers and academic researchers. Despite the fact that this data is anonymized and/or collected from a large pool of public-facing accounts, there are still a number of valid sources for apprehension regarding its execution and overall legitimacy.<br />
<br />
To reduce users’ qualms surrounding this type of activity and to ensure that the ethical validity of all such experimentation meets a sound moral standard, committees in the form of [[Wikipedia:Institutional_review_board|institutional review boards]] (IRBs) are organized across both industry and the social science disciplines of higher-learning. They are entrusted with the responsibility for reviewing, monitoring, and approving of any biomedical or behavioral research involving humans.<ref>Wikipedia: Institutional review board https://en.wikipedia.org/wiki/Institutional_review_board</ref> Because the natural extension of examining humans’ behavioral tendencies places us in the digital realm and thus satisfies the need for the aforementioned requisite oversight, here too IRBs serve a vital role in fulfilling the third-party ethical assessment of data collection and data manipulation for the sake of research driven advertising or academic inquiry.<br />
<br />
Firms often explicitly state in the terms and conditions of using their sites that they reserve the right to sell or use an individual's personally volunteered information toward serving their own business needs. Although this activity is controlled and kept within defined limits by consumer privacy laws, vigilant monitoring is necessary by the [[Wikipedia:Federal_Trade_Commission|Federal Trade Commission]] (FTC) and other such organizations to ensure that businesses are conducting themselves both fairly and lawfully. Because a user agreement is usually only displayed once (typically during the registration process), if it all, the gravity and seriousness ascribed to its contents is frequently undervalued by users, often relegated as unimportant and a low priority.<br />
<br />
As a majority of avid social media users are only concerned about a platform’s (superficially free) software and community assets -- expressed through its ability to cultivate social capital or the manner by which it advocates for a “more open and connected world” (e.g. Facebook) -- the embedded costs posed by data mining to users are subliminal and certainly not without their ramifications.<ref>Facebook, [http://www.facebook.com/advertising/ Advertising]</ref> Many users feel that the pursuit is an irresponsible practice, irrespective of the safeguards and stipulations found in user agreements, and consider it to be an activity in which social media firms should not engage.<ref>http://mashable.com/2011/02/25/data-mining-social-marketing/</ref> Often for that reason, or purely on the rationale that the sum total of one’s social contributions could be aggregated and packaged up for sale at any time to an outside party -- without due notification or option for opting out -- many people who choose not to participate on Web-based social platforms due so for concerns that mirror those convictions. Increasing the transparency of what data is mined and identifying when a user is particularly vulnerable (i.e. for disregarding privacy measures) has the potential to restore the necessary confidence in weary users.<br />
<br />
===Use Case #2: Business===<br />
Much like the value that can be derived from mining social sites, a wide array of businesses have a similar opportunity in leveraging data mining techniques for improving their respective processes and ultimately, economic returns. Reducing expenses and maximizing profits is a shared challenge among for-profit institutions. As a result, it is not uncommon for corporations spanning various disciplines to use mined data in conjunction with analytic and predictive methods for getting a more informed business perspective. Ethically, many of the issues that confront businesses with respect to data mining are similar to other contextual use cases: they revolve around the notion of privacy and the extent to which the resulting information can be shared with third-parties. Because the benefits of such activity can be used to “...reduce fraud, anticipate resource demand, increase acquisition and curb customer attrition,” the attractive nature of the process generally outweighs any lingering moral uncertainties.<ref>SAS Enterprise Analytics https://www.sas.com/en_us/software/analytics/enterprise-miner.html</ref><br />
<br />
===Use Case #3: Government===<br />
Privacy is a theme that pertains to almost every possible application of data mining as a mechanism for revealing information. This happens to be no different when discussed in the framework of government utilization. There are hundreds of available uses, with some of the most recently relevant being in the realm of predicting and and preventing acts of terrorism.<br />
<br />
===Use Case #4: Healthcare===<br />
As more information related to individuals’ health records becomes digitized, data mining will become significantly more useful within the healthcare field. Patients’ health records, when used in aggregate, will provide significant insight into the causes of specific illnesses. The success of data mining in this context hinges on the process of converting significant amounts of data into standardized digital formats on software systems with low storage and search costs.<br />
<br />
==See also==<br />
* [[Data Aggregation Online]]<br />
* [[Targeted Advertising (Online)]]<br />
* [[Geographic Information Systems]]<br />
*[[Recommender Systems]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Concepts]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Data_Mining&diff=56933Data Mining2016-04-21T16:59:22Z<p>Zzasuwa: /* Use Case #4: Healthcare */</p>
<hr />
<div>[[File:DiagramDataMining.jpg|right|thumb|350px|This illustration shows the role [[Wikipedia:Data Mining|Data Mining]] plays in processing information for business use.]]<br />
'''Data Mining''' is the act of analyzing data from various perspectives and summarizing it into useful information, which combines aspects of artificial intelligence, machine learning, statistics and database systems. Software is implemented as one of many analytical tools used to analyze data. Through data mining, data is presented to users from many different angles, in various categories and relationships. On a more technical term, data mining is the process of realizing correlations or patterns among large fields of relative databases.<ref name="palace">[http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/index.htm Palace, Bill. "What Is Data Mining?" Data Mining. Anderson Graduate School of Management at UCLA, Mar. 1996. Web. 16 Dec. 2011.]</ref><br />
<br />
Data mining is used in many fields such as business, marketing, engineering, medicine, and the music and gaming industry. Businesses employ data mining techniques to gather information on their customers to better advertise and target future customers, while data mining is used to recognize patterns and designate categories for work in gaming, medicine, and music. Because personal information is gathered and studied, ethical concerns arise from the lack of anonymity and privacy of the subjects used in data mining techniques.<br />
<br />
<br />
==Process of Data Mining==<br />
The process of Data Mining requires the extraction of useful information from large sets of data stored in a uniform way, often in a database. The type of information extracted is relative to the type of data available, and the purpose of the data mining project itself – the information extracted could be modeled or represented of the entire set of data for example if the objective was to draw a conclusion of the dataset as a whole. <ref>http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf</ref><br />
<br />
Data mining consists of multiple sub-tasks:<ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Olaru, C., & Wehenkel, L. (1999). Data mining. IEEE Computer Applications in Power, 12(3), 19-25. doi:10.1109/67.773801]</ref><ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Shen Bin, Liu Yuan, & Wang Xiaoyi. (2010). Research on data mining models for the internet of things. 2010 International Conference on Image Analysis and Signal Processing (IASP) (pp. 127-132). Presented at the 2010 International Conference on Image Analysis and Signal Processing (IASP), IEEE. doi:10.1109/IASP.2010.5476146]</ref><br />
<br />
===Anomaly Detection=== <br />
Also called deviation detection, this is the “data cleaning” process where data miners attempt to identify unusual circumstances within the data that may belie anomalies or errors. The process includes program-aided searches of the data, parsing the data through filters that check for certain cases, and searching for unexplainable patterns within the data. The anomaly detection process often requires checking how the data was aggregated, and if human error had an effect on the data present.<br />
<br />
===Clustering=== <br />
[[File:Clustered.png|right|thumb|350px|A database of songs that has been visually represented as clusters of data which best represent the categories of songs present (ie. hip-hop, punk).]]<br />
Situations arise within datasets where there is a desire to know if similarities exist within the data, but no mechanism for detecting them exists. Clustering procedures within data mining aim to find similar objects within the data or to analyze correlations between attributes of the data. Clustering is also used to find topographical similarities within data sets.<br />
<br />
===Classification=== <br />
Use of mined data requires the input and output information to be formatted in a discrete way in order for the information to be useful. Classifying traits of both the input and output of the data minimizes ambiguity, and helps define unique qualities of the data, and the meaning of the data itself. This process also helps discover unique relationships that exist between the data present in the database (the input data), and information that is being presented as conclusive of the data set (the output data). Classification can occur in many formats, from labeling, to building models which fit the particular data set explicitly.<br />
<br />
===Regression=== <br />
The building of a transparent model which fits the data, and adequately conveys the information contained within the data set is the objective of the regression step of data mining. The use of statistical methods is the most common approach within the regression step, because it yields discrete output. Regression modeling also attempts to minimize error within the other steps of the data mining process.<br />
<br />
===Summarization===<br />
This task aims at producing representative descriptions of the data which provides context for the data. The summary process can take multiple formats, the most common of which is numerical analysis, which provides statistical quantification for patterns in the data like the mean or standard deviation found within the data set. These results are often represented in graphical formats – such as histograms or scatter plots. Qualitative data can be summarized by giving a list of trends or frequencies within a set of data. These results are useful because they represent the entirety of the data set in a much more digestible format.<br />
<br />
==Data Mining Tools==<br />
<br />
As data mining is becoming more popular to sift through the massive amounts of data available online, new tools have been cropping up to make the job simpler. Google, for example, has released a free tool called Correlate<ref>http://www.fastcompany.com/1755287/google-correlate-tool-gives-marketers-powerful-new-data-mining-tools</ref> aimed at helping corporations utilize the Google search database to draw conclusions on consumer behavior. MIT and Harvard have recently collaborated in the creation of MINE, a tool of unprecedented power which can analyze more data for more complex patterns than any tool previously available. It is said to approach the quality of human examination in terms of being able to pick up on nonstandard or deceptive patterns<ref>http://www.decodedscience.com/data-mining-tool-advances-mine-ranks-multiple-patterns/7959</ref>.<br />
<br />
==Applications of Data Mining==<br />
===Examples===<br />
A simple example of data mining is analyzing a large population, such as University of Michigan students, and determining simple characteristics that the data has, such as the proportion of the student body that is from each ethnic background.<br />
<br />
Before the term "data mining" came into popular use, many businesses had already implemented its technology. They used powerful computers to comb through quantitative data from supermarket scanners, and analyzed the resulting data for market research purposes. This process have been immensely increasing the precision of analysis, and at the same time decreasing the cost of research.<ref name="palace"/><br />
<br />
===Business===<br />
Data mining is commonly used in business <ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:bookitem&rft.title=Introduction+to+Data+Mining+and+its+Applications&rft.atitle=Data+Mining+in+Sales+Marketing+and+Finance&rft.creator=Sumathi%2C+S&rft.creator=Sivanandam%2C+S+N&rft.series=Studies+in+Computational+Intelligence&rft.date=2006-01-01&rft.pub=Springer+Berlin+Heidelberg&rft.isbn=9783540343509&rft.issn=1860-949X&rft.volume=29&rft.spage=411&rft.epage=438&rft_id=info:doi/10.1007%2F978-3-540-34351-6_18&rft.externalDBID=n%2Fa&rft.externalDocID=978-3-540-34351-6_133505_Chap18 Data Mining in Sales Marketing and Finance. (2006). Introduction to Data Mining and its Applications (Vol. 29, pp. 411-438). Berlin, Heidelberg: Springer Berlin Heidelberg.]</ref> to determine what demographics are buying what products, and to help firms predict customer preferences. Online retailers often track the ways which their clients interact with the content on their site. This leads to an extensive collection of data that can be used by data mining procedures to model representative "personas" of future clients who will visit the site. This has meaningful value for companies, because information generated from data mining practices can lead to more "click-throughs" and potentially, more "conversions" (sales).<br />
<br />
===Science/Academics===<br />
Data Mining also plays a crucial role in scientific analysis. The objective of many large research studies is to find patterns in experimental data. Many data mining procedures allow for applicable modeling of data in a way that is much faster and easier than doing so by hand.<br />
<br />
===Other Uses===<br />
Data Mining techniques have been used in a variety of other matters, such as in admissions processes in universities and political campaigns. <ref>http://communities.washingtontimes.com/neighborhood/political-potpourri/2012/nov/17/how-obama-won/</ref><br />
<ref>http://www.tnr.com/article/110862/data-mining-the-ineffective-future-affirmative-action-in-education#</ref> <br />
<br />
==Ethical Implications==<br />
Data mining is an inherently morally-neutral action, as it is simply the practice of working with large stored data sets and the process itself does not account for the way that the data was generated or will inevitably be used. The ethics of data mining come under scrutiny when the type of data being mined is something that the individuals or specific entities subject to the mining feel should not be disclosed for others to view. Issues also arise when sufficiently robust restrictions are not in place to hide the private characteristics of an individual’s personal identity in the online environment. Likewise, when firms neglect to take adequately tested precautionary measures for protecting a customer’s data as part of a larger collective set of consumer information, they are effectively opening all their clients’ data up to the possibility of unprotected digital consumption and exploitation.<br />
<br />
===User Profiling and Identification===<br />
In an attempt to build accurate models, data miners in online environments occasionally utilize the personal information of individuals who visit their sites. The information generally collected includes, but is not limited to, geographic identifiers, records of past behavior, or information that can identify an individual by a range of other intimate attributes like user account details or IP addresses. Many users do not actively realize that their information is being collected and many question the ethics of using an individual’s private information without their outright and formal consent.<ref>http://www.ecommercetimes.com/story/52616.html</ref> Additionally, data mining practices and tools have made the automated profiling of groups and individuals an easy task for a growing number of the technically literate. Using data mining to create customer profiling questions also raises ethical concerns regarding these practices due to the associated risks it creates for discrimination, de-individualization, and information asymmetries.<ref>Schermer, B.W. (2011). The limits of privacy in automated profiling and data mining. Computer Law and Security Report, 27(1), pp. 45-52.</ref><br />
<br />
===Distortion of Truth===<br />
Second to privacy concerns, distorting or misrepresenting trends in data that are not actually there together account for the other main ethical issue on the topic of data mining. The result of human or programmatic mishaps, generating flawed associations between data points can be severely costly to reputations, economic footings, and the continued confidence in the reliability of the data analysis method.<br />
<br />
===Anonymity and Ownership===<br />
Many information systems contain data on individuals, so maintaining anonymity of the data and informing those whose information is being used, exactly how it is used and to what effect, is not only important but in many cases a legally binding matter. Allowing individuals the freedom to opt in or opt out of data mining processes is one way to ensure some form of ethical responsibility.<br />
<br />
Discussion regarding the role of data ownership emerges as a natural consequence to its collection from disparate sources and its subsequent composite processing, analysis, and summarization. A contested point in some circumstances is the differentiation between raw source data (input) and the proprietary information product (output). Because the former, when in aggregate form, represents the constituent building blocks of the latter, the resulting interdependency spawns many debates surrounding who, if anyone, rightfully owns each intermediate information good and the finished analysis. Boiling down to where lines are drawn with respect to voluntary (or involuntary) data relinquishment and the perception of residual value, the ground is ripe for dispute and ethical ambiguity.<br />
<br />
===Use Case #1: Social Networking===<br />
Mining data found on social networking sites poses a particular challenge, as a great deal of personal information can be found on individuals’ social media pages. It is not uncommon for social media firms like Facebook and Twitter to sell the information mined from users’ personal pages to advertisers and academic researchers. Despite the fact that this data is anonymized and/or collected from a large pool of public-facing accounts, there are still a number of valid sources for apprehension regarding its execution and overall legitimacy.<br />
<br />
To reduce users’ qualms surrounding this type of activity and to ensure that the ethical validity of all such experimentation meets a sound moral standard, committees in the form of [[Wikipedia:Institutional_review_board|institutional review boards]] (IRBs) are organized across both industry and the social science disciplines of higher-learning. They are entrusted with the responsibility for reviewing, monitoring, and approving of any biomedical or behavioral research involving humans.<ref>Wikipedia: Institutional review board https://en.wikipedia.org/wiki/Institutional_review_board</ref> Because the natural extension of examining humans’ behavioral tendencies places us in the digital realm and thus satisfies the need for the aforementioned requisite oversight, here too IRBs serve a vital role in fulfilling the third-party ethical assessment of data collection and data manipulation for the sake of research driven advertising or academic inquiry.<br />
<br />
Firms often explicitly state in the terms and conditions of using their sites that they reserve the right to sell or use an individual's personally volunteered information toward serving their own business needs. Although this activity is controlled and kept within defined limits by consumer privacy laws, vigilant monitoring is necessary by the [[Wikipedia:Federal_Trade_Commission|Federal Trade Commission]] (FTC) and other such organizations to ensure that businesses are conducting themselves both fairly and lawfully. Because a user agreement is usually only displayed once (typically during the registration process), if it all, the gravity and seriousness ascribed to its contents is frequently undervalued by users, often relegated as unimportant and a low priority.<br />
<br />
As a majority of avid social media users are only concerned about a platform’s (superficially free) software and community assets -- expressed through its ability to cultivate social capital or the manner by which it advocates for a “more open and connected world” (e.g. Facebook) -- the embedded costs posed by data mining to users are subliminal and certainly not without their ramifications. Many users feel that the pursuit is an irresponsible practice, irrespective of the safeguards and stipulations found in user agreements, and consider it to be an activity in which social media firms should not engage.<ref>Facebook, [http://www.facebook.com/advertising/ Advertising]</ref><ref>http://mashable.com/2011/02/25/data-mining-social-marketing/</ref> Often for that reason, or purely on the rationale that the sum total of one’s social contributions could be aggregated and packaged up for sale at any time to an outside party -- without due notification or option for opting out -- many people who choose not to participate on Web-based social platforms due so for concerns that mirror those convictions. Increasing the transparency of what data is mined and identifying when a user is particularly vulnerable (i.e. for disregarding privacy measures) has the potential to restore the necessary confidence in weary users.<br />
<br />
===Use Case #2: Business===<br />
Much like the value that can be derived from mining social sites, a wide array of businesses have a similar opportunity in leveraging data mining techniques for improving their respective processes and ultimately, economic returns. Reducing expenses and maximizing profits is a shared challenge among for-profit institutions. As a result, it is not uncommon for corporations spanning various disciplines to use mined data in conjunction with analytic and predictive methods for getting a more informed business perspective. Ethically, many of the issues that confront businesses with respect to data mining are similar to other contextual use cases: they revolve around the notion of privacy and the extent to which the resulting information can be shared with third-parties. Because the benefits of such activity can be used to “...reduce fraud, anticipate resource demand, increase acquisition and curb customer attrition,” the attractive nature of the process generally outweighs any lingering moral uncertainties.<ref>SAS Enterprise Analytics https://www.sas.com/en_us/software/analytics/enterprise-miner.html</ref><br />
<br />
===Use Case #3: Government===<br />
Privacy is a theme that pertains to almost every possible application of data mining as a mechanism for revealing information. This happens to be no different when discussed in the framework of government utilization. There are hundreds of available uses, with some of the most recently relevant being in the realm of predicting and and preventing acts of terrorism.<br />
<br />
===Use Case #4: Healthcare===<br />
As more information related to individuals’ health records becomes digitized, data mining will become significantly more useful within the healthcare field. Patients’ health records, when used in aggregate, will provide significant insight into the causes of specific illnesses. The success of data mining in this context hinges on the process of converting significant amounts of data into standardized digital formats on software systems with low storage and search costs.<br />
<br />
==See also==<br />
* [[Data Aggregation Online]]<br />
* [[Targeted Advertising (Online)]]<br />
* [[Geographic Information Systems]]<br />
*[[Recommender Systems]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Concepts]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Data_Mining&diff=56932Data Mining2016-04-21T16:57:11Z<p>Zzasuwa: /* Use Case #4: Healthcare */</p>
<hr />
<div>[[File:DiagramDataMining.jpg|right|thumb|350px|This illustration shows the role [[Wikipedia:Data Mining|Data Mining]] plays in processing information for business use.]]<br />
'''Data Mining''' is the act of analyzing data from various perspectives and summarizing it into useful information, which combines aspects of artificial intelligence, machine learning, statistics and database systems. Software is implemented as one of many analytical tools used to analyze data. Through data mining, data is presented to users from many different angles, in various categories and relationships. On a more technical term, data mining is the process of realizing correlations or patterns among large fields of relative databases.<ref name="palace">[http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/index.htm Palace, Bill. "What Is Data Mining?" Data Mining. Anderson Graduate School of Management at UCLA, Mar. 1996. Web. 16 Dec. 2011.]</ref><br />
<br />
Data mining is used in many fields such as business, marketing, engineering, medicine, and the music and gaming industry. Businesses employ data mining techniques to gather information on their customers to better advertise and target future customers, while data mining is used to recognize patterns and designate categories for work in gaming, medicine, and music. Because personal information is gathered and studied, ethical concerns arise from the lack of anonymity and privacy of the subjects used in data mining techniques.<br />
<br />
<br />
==Process of Data Mining==<br />
The process of Data Mining requires the extraction of useful information from large sets of data stored in a uniform way, often in a database. The type of information extracted is relative to the type of data available, and the purpose of the data mining project itself – the information extracted could be modeled or represented of the entire set of data for example if the objective was to draw a conclusion of the dataset as a whole. <ref>http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf</ref><br />
<br />
Data mining consists of multiple sub-tasks:<ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Olaru, C., & Wehenkel, L. (1999). Data mining. IEEE Computer Applications in Power, 12(3), 19-25. doi:10.1109/67.773801]</ref><ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Shen Bin, Liu Yuan, & Wang Xiaoyi. (2010). Research on data mining models for the internet of things. 2010 International Conference on Image Analysis and Signal Processing (IASP) (pp. 127-132). Presented at the 2010 International Conference on Image Analysis and Signal Processing (IASP), IEEE. doi:10.1109/IASP.2010.5476146]</ref><br />
<br />
===Anomaly Detection=== <br />
Also called deviation detection, this is the “data cleaning” process where data miners attempt to identify unusual circumstances within the data that may belie anomalies or errors. The process includes program-aided searches of the data, parsing the data through filters that check for certain cases, and searching for unexplainable patterns within the data. The anomaly detection process often requires checking how the data was aggregated, and if human error had an effect on the data present.<br />
<br />
===Clustering=== <br />
[[File:Clustered.png|right|thumb|350px|A database of songs that has been visually represented as clusters of data which best represent the categories of songs present (ie. hip-hop, punk).]]<br />
Situations arise within datasets where there is a desire to know if similarities exist within the data, but no mechanism for detecting them exists. Clustering procedures within data mining aim to find similar objects within the data or to analyze correlations between attributes of the data. Clustering is also used to find topographical similarities within data sets.<br />
<br />
===Classification=== <br />
Use of mined data requires the input and output information to be formatted in a discrete way in order for the information to be useful. Classifying traits of both the input and output of the data minimizes ambiguity, and helps define unique qualities of the data, and the meaning of the data itself. This process also helps discover unique relationships that exist between the data present in the database (the input data), and information that is being presented as conclusive of the data set (the output data). Classification can occur in many formats, from labeling, to building models which fit the particular data set explicitly.<br />
<br />
===Regression=== <br />
The building of a transparent model which fits the data, and adequately conveys the information contained within the data set is the objective of the regression step of data mining. The use of statistical methods is the most common approach within the regression step, because it yields discrete output. Regression modeling also attempts to minimize error within the other steps of the data mining process.<br />
<br />
===Summarization===<br />
This task aims at producing representative descriptions of the data which provides context for the data. The summary process can take multiple formats, the most common of which is numerical analysis, which provides statistical quantification for patterns in the data like the mean or standard deviation found within the data set. These results are often represented in graphical formats – such as histograms or scatter plots. Qualitative data can be summarized by giving a list of trends or frequencies within a set of data. These results are useful because they represent the entirety of the data set in a much more digestible format.<br />
<br />
==Data Mining Tools==<br />
<br />
As data mining is becoming more popular to sift through the massive amounts of data available online, new tools have been cropping up to make the job simpler. Google, for example, has released a free tool called Correlate<ref>http://www.fastcompany.com/1755287/google-correlate-tool-gives-marketers-powerful-new-data-mining-tools</ref> aimed at helping corporations utilize the Google search database to draw conclusions on consumer behavior. MIT and Harvard have recently collaborated in the creation of MINE, a tool of unprecedented power which can analyze more data for more complex patterns than any tool previously available. It is said to approach the quality of human examination in terms of being able to pick up on nonstandard or deceptive patterns<ref>http://www.decodedscience.com/data-mining-tool-advances-mine-ranks-multiple-patterns/7959</ref>.<br />
<br />
==Applications of Data Mining==<br />
===Examples===<br />
A simple example of data mining is analyzing a large population, such as University of Michigan students, and determining simple characteristics that the data has, such as the proportion of the student body that is from each ethnic background.<br />
<br />
Before the term "data mining" came into popular use, many businesses had already implemented its technology. They used powerful computers to comb through quantitative data from supermarket scanners, and analyzed the resulting data for market research purposes. This process have been immensely increasing the precision of analysis, and at the same time decreasing the cost of research.<ref name="palace"/><br />
<br />
===Business===<br />
Data mining is commonly used in business <ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:bookitem&rft.title=Introduction+to+Data+Mining+and+its+Applications&rft.atitle=Data+Mining+in+Sales+Marketing+and+Finance&rft.creator=Sumathi%2C+S&rft.creator=Sivanandam%2C+S+N&rft.series=Studies+in+Computational+Intelligence&rft.date=2006-01-01&rft.pub=Springer+Berlin+Heidelberg&rft.isbn=9783540343509&rft.issn=1860-949X&rft.volume=29&rft.spage=411&rft.epage=438&rft_id=info:doi/10.1007%2F978-3-540-34351-6_18&rft.externalDBID=n%2Fa&rft.externalDocID=978-3-540-34351-6_133505_Chap18 Data Mining in Sales Marketing and Finance. (2006). Introduction to Data Mining and its Applications (Vol. 29, pp. 411-438). Berlin, Heidelberg: Springer Berlin Heidelberg.]</ref> to determine what demographics are buying what products, and to help firms predict customer preferences. Online retailers often track the ways which their clients interact with the content on their site. This leads to an extensive collection of data that can be used by data mining procedures to model representative "personas" of future clients who will visit the site. This has meaningful value for companies, because information generated from data mining practices can lead to more "click-throughs" and potentially, more "conversions" (sales).<br />
<br />
===Science/Academics===<br />
Data Mining also plays a crucial role in scientific analysis. The objective of many large research studies is to find patterns in experimental data. Many data mining procedures allow for applicable modeling of data in a way that is much faster and easier than doing so by hand.<br />
<br />
===Other Uses===<br />
Data Mining techniques have been used in a variety of other matters, such as in admissions processes in universities and political campaigns. <ref>http://communities.washingtontimes.com/neighborhood/political-potpourri/2012/nov/17/how-obama-won/</ref><br />
<ref>http://www.tnr.com/article/110862/data-mining-the-ineffective-future-affirmative-action-in-education#</ref> <br />
<br />
==Ethical Implications==<br />
Data mining is an inherently morally-neutral action, as it is simply the practice of working with large stored data sets and the process itself does not account for the way that the data was generated or will inevitably be used. The ethics of data mining come under scrutiny when the type of data being mined is something that the individuals or specific entities subject to the mining feel should not be disclosed for others to view. Issues also arise when sufficiently robust restrictions are not in place to hide the private characteristics of an individual’s personal identity in the online environment. Likewise, when firms neglect to take adequately tested precautionary measures for protecting a customer’s data as part of a larger collective set of consumer information, they are effectively opening all their clients’ data up to the possibility of unprotected digital consumption and exploitation.<br />
<br />
===User Profiling and Identification===<br />
In an attempt to build accurate models, data miners in online environments occasionally utilize the personal information of individuals who visit their sites. The information generally collected includes, but is not limited to, geographic identifiers, records of past behavior, or information that can identify an individual by a range of other intimate attributes like user account details or IP addresses. Many users do not actively realize that their information is being collected and many question the ethics of using an individual’s private information without their outright and formal consent.<ref>http://www.ecommercetimes.com/story/52616.html</ref> Additionally, data mining practices and tools have made the automated profiling of groups and individuals an easy task for a growing number of the technically literate. Using data mining to create customer profiling questions also raises ethical concerns regarding these practices due to the associated risks it creates for discrimination, de-individualization, and information asymmetries.<ref>Schermer, B.W. (2011). The limits of privacy in automated profiling and data mining. Computer Law and Security Report, 27(1), pp. 45-52.</ref><br />
<br />
===Distortion of Truth===<br />
Second to privacy concerns, distorting or misrepresenting trends in data that are not actually there together account for the other main ethical issue on the topic of data mining. The result of human or programmatic mishaps, generating flawed associations between data points can be severely costly to reputations, economic footings, and the continued confidence in the reliability of the data analysis method.<br />
<br />
===Anonymity and Ownership===<br />
Many information systems contain data on individuals, so maintaining anonymity of the data and informing those whose information is being used, exactly how it is used and to what effect, is not only important but in many cases a legally binding matter. Allowing individuals the freedom to opt in or opt out of data mining processes is one way to ensure some form of ethical responsibility.<br />
<br />
Discussion regarding the role of data ownership emerges as a natural consequence to its collection from disparate sources and its subsequent composite processing, analysis, and summarization. A contested point in some circumstances is the differentiation between raw source data (input) and the proprietary information product (output). Because the former, when in aggregate form, represents the constituent building blocks of the latter, the resulting interdependency spawns many debates surrounding who, if anyone, rightfully owns each intermediate information good and the finished analysis. Boiling down to where lines are drawn with respect to voluntary (or involuntary) data relinquishment and the perception of residual value, the ground is ripe for dispute and ethical ambiguity.<br />
<br />
===Use Case #1: Social Networking===<br />
Mining data found on social networking sites poses a particular challenge, as a great deal of personal information can be found on individuals’ social media pages. It is not uncommon for social media firms like Facebook and Twitter to sell the information mined from users’ personal pages to advertisers and academic researchers. Despite the fact that this data is anonymized and/or collected from a large pool of public-facing accounts, there are still a number of valid sources for apprehension regarding its execution and overall legitimacy.<br />
<br />
To reduce users’ qualms surrounding this type of activity and to ensure that the ethical validity of all such experimentation meets a sound moral standard, committees in the form of [[Wikipedia:Institutional_review_board|institutional review boards]] (IRBs) are organized across both industry and the social science disciplines of higher-learning. They are entrusted with the responsibility for reviewing, monitoring, and approving of any biomedical or behavioral research involving humans.<ref>Wikipedia: Institutional review board https://en.wikipedia.org/wiki/Institutional_review_board</ref> Because the natural extension of examining humans’ behavioral tendencies places us in the digital realm and thus satisfies the need for the aforementioned requisite oversight, here too IRBs serve a vital role in fulfilling the third-party ethical assessment of data collection and data manipulation for the sake of research driven advertising or academic inquiry.<br />
<br />
Firms often explicitly state in the terms and conditions of using their sites that they reserve the right to sell or use an individual's personally volunteered information toward serving their own business needs. Although this activity is controlled and kept within defined limits by consumer privacy laws, vigilant monitoring is necessary by the [[Wikipedia:Federal_Trade_Commission|Federal Trade Commission]] (FTC) and other such organizations to ensure that businesses are conducting themselves both fairly and lawfully. Because a user agreement is usually only displayed once (typically during the registration process), if it all, the gravity and seriousness ascribed to its contents is frequently undervalued by users, often relegated as unimportant and a low priority.<br />
<br />
As a majority of avid social media users are only concerned about a platform’s (superficially free) software and community assets -- expressed through its ability to cultivate social capital or the manner by which it advocates for a “more open and connected world” (e.g. Facebook) -- the embedded costs posed by data mining to users are subliminal and certainly not without their ramifications. Many users feel that the pursuit is an irresponsible practice, irrespective of the safeguards and stipulations found in user agreements, and consider it to be an activity in which social media firms should not engage.<ref>Facebook, [http://www.facebook.com/advertising/ Advertising]</ref><ref>http://mashable.com/2011/02/25/data-mining-social-marketing/</ref> Often for that reason, or purely on the rationale that the sum total of one’s social contributions could be aggregated and packaged up for sale at any time to an outside party -- without due notification or option for opting out -- many people who choose not to participate on Web-based social platforms due so for concerns that mirror those convictions. Increasing the transparency of what data is mined and identifying when a user is particularly vulnerable (i.e. for disregarding privacy measures) has the potential to restore the necessary confidence in weary users.<br />
<br />
===Use Case #2: Business===<br />
Much like the value that can be derived from mining social sites, a wide array of businesses have a similar opportunity in leveraging data mining techniques for improving their respective processes and ultimately, economic returns. Reducing expenses and maximizing profits is a shared challenge among for-profit institutions. As a result, it is not uncommon for corporations spanning various disciplines to use mined data in conjunction with analytic and predictive methods for getting a more informed business perspective. Ethically, many of the issues that confront businesses with respect to data mining are similar to other contextual use cases: they revolve around the notion of privacy and the extent to which the resulting information can be shared with third-parties. Because the benefits of such activity can be used to “...reduce fraud, anticipate resource demand, increase acquisition and curb customer attrition,” the attractive nature of the process generally outweighs any lingering moral uncertainties.<ref>SAS Enterprise Analytics https://www.sas.com/en_us/software/analytics/enterprise-miner.html</ref><br />
<br />
===Use Case #3: Government===<br />
Privacy is a theme that pertains to almost every possible application of data mining as a mechanism for revealing information. This happens to be no different when discussed in the framework of government utilization. There are hundreds of available uses, with some of the most recently relevant being in the realm of predicting and and preventing acts of terrorism.<br />
<br />
===Use Case #4: Healthcare===<br />
As more information related to individuals’ health records becomes digitized, data mining will become significantly more useful within the healthcare field. Patients’ health records, while used in aggregate, will provide significant insight into causes of specific illnesses. The success of data mining in this context hinges on the process of converting significant amounts of data into standardized digital formats on software systems with low storage and search costs.<br />
<br />
==See also==<br />
* [[Data Aggregation Online]]<br />
* [[Targeted Advertising (Online)]]<br />
* [[Geographic Information Systems]]<br />
*[[Recommender Systems]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Concepts]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Data_Mining&diff=56931Data Mining2016-04-21T16:56:10Z<p>Zzasuwa: /* Use Case #3: Government */</p>
<hr />
<div>[[File:DiagramDataMining.jpg|right|thumb|350px|This illustration shows the role [[Wikipedia:Data Mining|Data Mining]] plays in processing information for business use.]]<br />
'''Data Mining''' is the act of analyzing data from various perspectives and summarizing it into useful information, which combines aspects of artificial intelligence, machine learning, statistics and database systems. Software is implemented as one of many analytical tools used to analyze data. Through data mining, data is presented to users from many different angles, in various categories and relationships. On a more technical term, data mining is the process of realizing correlations or patterns among large fields of relative databases.<ref name="palace">[http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/index.htm Palace, Bill. "What Is Data Mining?" Data Mining. Anderson Graduate School of Management at UCLA, Mar. 1996. Web. 16 Dec. 2011.]</ref><br />
<br />
Data mining is used in many fields such as business, marketing, engineering, medicine, and the music and gaming industry. Businesses employ data mining techniques to gather information on their customers to better advertise and target future customers, while data mining is used to recognize patterns and designate categories for work in gaming, medicine, and music. Because personal information is gathered and studied, ethical concerns arise from the lack of anonymity and privacy of the subjects used in data mining techniques.<br />
<br />
<br />
==Process of Data Mining==<br />
The process of Data Mining requires the extraction of useful information from large sets of data stored in a uniform way, often in a database. The type of information extracted is relative to the type of data available, and the purpose of the data mining project itself – the information extracted could be modeled or represented of the entire set of data for example if the objective was to draw a conclusion of the dataset as a whole. <ref>http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf</ref><br />
<br />
Data mining consists of multiple sub-tasks:<ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Olaru, C., & Wehenkel, L. (1999). Data mining. IEEE Computer Applications in Power, 12(3), 19-25. doi:10.1109/67.773801]</ref><ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Shen Bin, Liu Yuan, & Wang Xiaoyi. (2010). Research on data mining models for the internet of things. 2010 International Conference on Image Analysis and Signal Processing (IASP) (pp. 127-132). Presented at the 2010 International Conference on Image Analysis and Signal Processing (IASP), IEEE. doi:10.1109/IASP.2010.5476146]</ref><br />
<br />
===Anomaly Detection=== <br />
Also called deviation detection, this is the “data cleaning” process where data miners attempt to identify unusual circumstances within the data that may belie anomalies or errors. The process includes program-aided searches of the data, parsing the data through filters that check for certain cases, and searching for unexplainable patterns within the data. The anomaly detection process often requires checking how the data was aggregated, and if human error had an effect on the data present.<br />
<br />
===Clustering=== <br />
[[File:Clustered.png|right|thumb|350px|A database of songs that has been visually represented as clusters of data which best represent the categories of songs present (ie. hip-hop, punk).]]<br />
Situations arise within datasets where there is a desire to know if similarities exist within the data, but no mechanism for detecting them exists. Clustering procedures within data mining aim to find similar objects within the data or to analyze correlations between attributes of the data. Clustering is also used to find topographical similarities within data sets.<br />
<br />
===Classification=== <br />
Use of mined data requires the input and output information to be formatted in a discrete way in order for the information to be useful. Classifying traits of both the input and output of the data minimizes ambiguity, and helps define unique qualities of the data, and the meaning of the data itself. This process also helps discover unique relationships that exist between the data present in the database (the input data), and information that is being presented as conclusive of the data set (the output data). Classification can occur in many formats, from labeling, to building models which fit the particular data set explicitly.<br />
<br />
===Regression=== <br />
The building of a transparent model which fits the data, and adequately conveys the information contained within the data set is the objective of the regression step of data mining. The use of statistical methods is the most common approach within the regression step, because it yields discrete output. Regression modeling also attempts to minimize error within the other steps of the data mining process.<br />
<br />
===Summarization===<br />
This task aims at producing representative descriptions of the data which provides context for the data. The summary process can take multiple formats, the most common of which is numerical analysis, which provides statistical quantification for patterns in the data like the mean or standard deviation found within the data set. These results are often represented in graphical formats – such as histograms or scatter plots. Qualitative data can be summarized by giving a list of trends or frequencies within a set of data. These results are useful because they represent the entirety of the data set in a much more digestible format.<br />
<br />
==Data Mining Tools==<br />
<br />
As data mining is becoming more popular to sift through the massive amounts of data available online, new tools have been cropping up to make the job simpler. Google, for example, has released a free tool called Correlate<ref>http://www.fastcompany.com/1755287/google-correlate-tool-gives-marketers-powerful-new-data-mining-tools</ref> aimed at helping corporations utilize the Google search database to draw conclusions on consumer behavior. MIT and Harvard have recently collaborated in the creation of MINE, a tool of unprecedented power which can analyze more data for more complex patterns than any tool previously available. It is said to approach the quality of human examination in terms of being able to pick up on nonstandard or deceptive patterns<ref>http://www.decodedscience.com/data-mining-tool-advances-mine-ranks-multiple-patterns/7959</ref>.<br />
<br />
==Applications of Data Mining==<br />
===Examples===<br />
A simple example of data mining is analyzing a large population, such as University of Michigan students, and determining simple characteristics that the data has, such as the proportion of the student body that is from each ethnic background.<br />
<br />
Before the term "data mining" came into popular use, many businesses had already implemented its technology. They used powerful computers to comb through quantitative data from supermarket scanners, and analyzed the resulting data for market research purposes. This process have been immensely increasing the precision of analysis, and at the same time decreasing the cost of research.<ref name="palace"/><br />
<br />
===Business===<br />
Data mining is commonly used in business <ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:bookitem&rft.title=Introduction+to+Data+Mining+and+its+Applications&rft.atitle=Data+Mining+in+Sales+Marketing+and+Finance&rft.creator=Sumathi%2C+S&rft.creator=Sivanandam%2C+S+N&rft.series=Studies+in+Computational+Intelligence&rft.date=2006-01-01&rft.pub=Springer+Berlin+Heidelberg&rft.isbn=9783540343509&rft.issn=1860-949X&rft.volume=29&rft.spage=411&rft.epage=438&rft_id=info:doi/10.1007%2F978-3-540-34351-6_18&rft.externalDBID=n%2Fa&rft.externalDocID=978-3-540-34351-6_133505_Chap18 Data Mining in Sales Marketing and Finance. (2006). Introduction to Data Mining and its Applications (Vol. 29, pp. 411-438). Berlin, Heidelberg: Springer Berlin Heidelberg.]</ref> to determine what demographics are buying what products, and to help firms predict customer preferences. Online retailers often track the ways which their clients interact with the content on their site. This leads to an extensive collection of data that can be used by data mining procedures to model representative "personas" of future clients who will visit the site. This has meaningful value for companies, because information generated from data mining practices can lead to more "click-throughs" and potentially, more "conversions" (sales).<br />
<br />
===Science/Academics===<br />
Data Mining also plays a crucial role in scientific analysis. The objective of many large research studies is to find patterns in experimental data. Many data mining procedures allow for applicable modeling of data in a way that is much faster and easier than doing so by hand.<br />
<br />
===Other Uses===<br />
Data Mining techniques have been used in a variety of other matters, such as in admissions processes in universities and political campaigns. <ref>http://communities.washingtontimes.com/neighborhood/political-potpourri/2012/nov/17/how-obama-won/</ref><br />
<ref>http://www.tnr.com/article/110862/data-mining-the-ineffective-future-affirmative-action-in-education#</ref> <br />
<br />
==Ethical Implications==<br />
Data mining is an inherently morally-neutral action, as it is simply the practice of working with large stored data sets and the process itself does not account for the way that the data was generated or will inevitably be used. The ethics of data mining come under scrutiny when the type of data being mined is something that the individuals or specific entities subject to the mining feel should not be disclosed for others to view. Issues also arise when sufficiently robust restrictions are not in place to hide the private characteristics of an individual’s personal identity in the online environment. Likewise, when firms neglect to take adequately tested precautionary measures for protecting a customer’s data as part of a larger collective set of consumer information, they are effectively opening all their clients’ data up to the possibility of unprotected digital consumption and exploitation.<br />
<br />
===User Profiling and Identification===<br />
In an attempt to build accurate models, data miners in online environments occasionally utilize the personal information of individuals who visit their sites. The information generally collected includes, but is not limited to, geographic identifiers, records of past behavior, or information that can identify an individual by a range of other intimate attributes like user account details or IP addresses. Many users do not actively realize that their information is being collected and many question the ethics of using an individual’s private information without their outright and formal consent.<ref>http://www.ecommercetimes.com/story/52616.html</ref> Additionally, data mining practices and tools have made the automated profiling of groups and individuals an easy task for a growing number of the technically literate. Using data mining to create customer profiling questions also raises ethical concerns regarding these practices due to the associated risks it creates for discrimination, de-individualization, and information asymmetries.<ref>Schermer, B.W. (2011). The limits of privacy in automated profiling and data mining. Computer Law and Security Report, 27(1), pp. 45-52.</ref><br />
<br />
===Distortion of Truth===<br />
Second to privacy concerns, distorting or misrepresenting trends in data that are not actually there together account for the other main ethical issue on the topic of data mining. The result of human or programmatic mishaps, generating flawed associations between data points can be severely costly to reputations, economic footings, and the continued confidence in the reliability of the data analysis method.<br />
<br />
===Anonymity and Ownership===<br />
Many information systems contain data on individuals, so maintaining anonymity of the data and informing those whose information is being used, exactly how it is used and to what effect, is not only important but in many cases a legally binding matter. Allowing individuals the freedom to opt in or opt out of data mining processes is one way to ensure some form of ethical responsibility.<br />
<br />
Discussion regarding the role of data ownership emerges as a natural consequence to its collection from disparate sources and its subsequent composite processing, analysis, and summarization. A contested point in some circumstances is the differentiation between raw source data (input) and the proprietary information product (output). Because the former, when in aggregate form, represents the constituent building blocks of the latter, the resulting interdependency spawns many debates surrounding who, if anyone, rightfully owns each intermediate information good and the finished analysis. Boiling down to where lines are drawn with respect to voluntary (or involuntary) data relinquishment and the perception of residual value, the ground is ripe for dispute and ethical ambiguity.<br />
<br />
===Use Case #1: Social Networking===<br />
Mining data found on social networking sites poses a particular challenge, as a great deal of personal information can be found on individuals’ social media pages. It is not uncommon for social media firms like Facebook and Twitter to sell the information mined from users’ personal pages to advertisers and academic researchers. Despite the fact that this data is anonymized and/or collected from a large pool of public-facing accounts, there are still a number of valid sources for apprehension regarding its execution and overall legitimacy.<br />
<br />
To reduce users’ qualms surrounding this type of activity and to ensure that the ethical validity of all such experimentation meets a sound moral standard, committees in the form of [[Wikipedia:Institutional_review_board|institutional review boards]] (IRBs) are organized across both industry and the social science disciplines of higher-learning. They are entrusted with the responsibility for reviewing, monitoring, and approving of any biomedical or behavioral research involving humans.<ref>Wikipedia: Institutional review board https://en.wikipedia.org/wiki/Institutional_review_board</ref> Because the natural extension of examining humans’ behavioral tendencies places us in the digital realm and thus satisfies the need for the aforementioned requisite oversight, here too IRBs serve a vital role in fulfilling the third-party ethical assessment of data collection and data manipulation for the sake of research driven advertising or academic inquiry.<br />
<br />
Firms often explicitly state in the terms and conditions of using their sites that they reserve the right to sell or use an individual's personally volunteered information toward serving their own business needs. Although this activity is controlled and kept within defined limits by consumer privacy laws, vigilant monitoring is necessary by the [[Wikipedia:Federal_Trade_Commission|Federal Trade Commission]] (FTC) and other such organizations to ensure that businesses are conducting themselves both fairly and lawfully. Because a user agreement is usually only displayed once (typically during the registration process), if it all, the gravity and seriousness ascribed to its contents is frequently undervalued by users, often relegated as unimportant and a low priority.<br />
<br />
As a majority of avid social media users are only concerned about a platform’s (superficially free) software and community assets -- expressed through its ability to cultivate social capital or the manner by which it advocates for a “more open and connected world” (e.g. Facebook) -- the embedded costs posed by data mining to users are subliminal and certainly not without their ramifications. Many users feel that the pursuit is an irresponsible practice, irrespective of the safeguards and stipulations found in user agreements, and consider it to be an activity in which social media firms should not engage.<ref>Facebook, [http://www.facebook.com/advertising/ Advertising]</ref><ref>http://mashable.com/2011/02/25/data-mining-social-marketing/</ref> Often for that reason, or purely on the rationale that the sum total of one’s social contributions could be aggregated and packaged up for sale at any time to an outside party -- without due notification or option for opting out -- many people who choose not to participate on Web-based social platforms due so for concerns that mirror those convictions. Increasing the transparency of what data is mined and identifying when a user is particularly vulnerable (i.e. for disregarding privacy measures) has the potential to restore the necessary confidence in weary users.<br />
<br />
===Use Case #2: Business===<br />
Much like the value that can be derived from mining social sites, a wide array of businesses have a similar opportunity in leveraging data mining techniques for improving their respective processes and ultimately, economic returns. Reducing expenses and maximizing profits is a shared challenge among for-profit institutions. As a result, it is not uncommon for corporations spanning various disciplines to use mined data in conjunction with analytic and predictive methods for getting a more informed business perspective. Ethically, many of the issues that confront businesses with respect to data mining are similar to other contextual use cases: they revolve around the notion of privacy and the extent to which the resulting information can be shared with third-parties. Because the benefits of such activity can be used to “...reduce fraud, anticipate resource demand, increase acquisition and curb customer attrition,” the attractive nature of the process generally outweighs any lingering moral uncertainties.<ref>SAS Enterprise Analytics https://www.sas.com/en_us/software/analytics/enterprise-miner.html</ref><br />
<br />
===Use Case #3: Government===<br />
Privacy is a theme that pertains to almost every possible application of data mining as a mechanism for revealing information. This happens to be no different when discussed in the framework of government utilization. There are hundreds of available uses, with some of the most recently relevant being in the realm of predicting and and preventing acts of terrorism.<br />
<br />
===Use Case #4: Healthcare===<br />
As more information related to individuals’ health records becomes digitized, data mining will become significantly more useful within the healthcare field. Patients’ health records, while used in aggregate, will provide significant insight into causes of specific illnesses. The success of data mining in this context hinges on the process of converting significant amounts of data into standardized digital formats on software frameworks with low storage and search costs.<br />
<br />
==See also==<br />
* [[Data Aggregation Online]]<br />
* [[Targeted Advertising (Online)]]<br />
* [[Geographic Information Systems]]<br />
*[[Recommender Systems]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Concepts]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Data_Mining&diff=56930Data Mining2016-04-21T16:51:24Z<p>Zzasuwa: /* Use Case #2: Business */</p>
<hr />
<div>[[File:DiagramDataMining.jpg|right|thumb|350px|This illustration shows the role [[Wikipedia:Data Mining|Data Mining]] plays in processing information for business use.]]<br />
'''Data Mining''' is the act of analyzing data from various perspectives and summarizing it into useful information, which combines aspects of artificial intelligence, machine learning, statistics and database systems. Software is implemented as one of many analytical tools used to analyze data. Through data mining, data is presented to users from many different angles, in various categories and relationships. On a more technical term, data mining is the process of realizing correlations or patterns among large fields of relative databases.<ref name="palace">[http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/index.htm Palace, Bill. "What Is Data Mining?" Data Mining. Anderson Graduate School of Management at UCLA, Mar. 1996. Web. 16 Dec. 2011.]</ref><br />
<br />
Data mining is used in many fields such as business, marketing, engineering, medicine, and the music and gaming industry. Businesses employ data mining techniques to gather information on their customers to better advertise and target future customers, while data mining is used to recognize patterns and designate categories for work in gaming, medicine, and music. Because personal information is gathered and studied, ethical concerns arise from the lack of anonymity and privacy of the subjects used in data mining techniques.<br />
<br />
<br />
==Process of Data Mining==<br />
The process of Data Mining requires the extraction of useful information from large sets of data stored in a uniform way, often in a database. The type of information extracted is relative to the type of data available, and the purpose of the data mining project itself – the information extracted could be modeled or represented of the entire set of data for example if the objective was to draw a conclusion of the dataset as a whole. <ref>http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf</ref><br />
<br />
Data mining consists of multiple sub-tasks:<ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Olaru, C., & Wehenkel, L. (1999). Data mining. IEEE Computer Applications in Power, 12(3), 19-25. doi:10.1109/67.773801]</ref><ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Shen Bin, Liu Yuan, & Wang Xiaoyi. (2010). Research on data mining models for the internet of things. 2010 International Conference on Image Analysis and Signal Processing (IASP) (pp. 127-132). Presented at the 2010 International Conference on Image Analysis and Signal Processing (IASP), IEEE. doi:10.1109/IASP.2010.5476146]</ref><br />
<br />
===Anomaly Detection=== <br />
Also called deviation detection, this is the “data cleaning” process where data miners attempt to identify unusual circumstances within the data that may belie anomalies or errors. The process includes program-aided searches of the data, parsing the data through filters that check for certain cases, and searching for unexplainable patterns within the data. The anomaly detection process often requires checking how the data was aggregated, and if human error had an effect on the data present.<br />
<br />
===Clustering=== <br />
[[File:Clustered.png|right|thumb|350px|A database of songs that has been visually represented as clusters of data which best represent the categories of songs present (ie. hip-hop, punk).]]<br />
Situations arise within datasets where there is a desire to know if similarities exist within the data, but no mechanism for detecting them exists. Clustering procedures within data mining aim to find similar objects within the data or to analyze correlations between attributes of the data. Clustering is also used to find topographical similarities within data sets.<br />
<br />
===Classification=== <br />
Use of mined data requires the input and output information to be formatted in a discrete way in order for the information to be useful. Classifying traits of both the input and output of the data minimizes ambiguity, and helps define unique qualities of the data, and the meaning of the data itself. This process also helps discover unique relationships that exist between the data present in the database (the input data), and information that is being presented as conclusive of the data set (the output data). Classification can occur in many formats, from labeling, to building models which fit the particular data set explicitly.<br />
<br />
===Regression=== <br />
The building of a transparent model which fits the data, and adequately conveys the information contained within the data set is the objective of the regression step of data mining. The use of statistical methods is the most common approach within the regression step, because it yields discrete output. Regression modeling also attempts to minimize error within the other steps of the data mining process.<br />
<br />
===Summarization===<br />
This task aims at producing representative descriptions of the data which provides context for the data. The summary process can take multiple formats, the most common of which is numerical analysis, which provides statistical quantification for patterns in the data like the mean or standard deviation found within the data set. These results are often represented in graphical formats – such as histograms or scatter plots. Qualitative data can be summarized by giving a list of trends or frequencies within a set of data. These results are useful because they represent the entirety of the data set in a much more digestible format.<br />
<br />
==Data Mining Tools==<br />
<br />
As data mining is becoming more popular to sift through the massive amounts of data available online, new tools have been cropping up to make the job simpler. Google, for example, has released a free tool called Correlate<ref>http://www.fastcompany.com/1755287/google-correlate-tool-gives-marketers-powerful-new-data-mining-tools</ref> aimed at helping corporations utilize the Google search database to draw conclusions on consumer behavior. MIT and Harvard have recently collaborated in the creation of MINE, a tool of unprecedented power which can analyze more data for more complex patterns than any tool previously available. It is said to approach the quality of human examination in terms of being able to pick up on nonstandard or deceptive patterns<ref>http://www.decodedscience.com/data-mining-tool-advances-mine-ranks-multiple-patterns/7959</ref>.<br />
<br />
==Applications of Data Mining==<br />
===Examples===<br />
A simple example of data mining is analyzing a large population, such as University of Michigan students, and determining simple characteristics that the data has, such as the proportion of the student body that is from each ethnic background.<br />
<br />
Before the term "data mining" came into popular use, many businesses had already implemented its technology. They used powerful computers to comb through quantitative data from supermarket scanners, and analyzed the resulting data for market research purposes. This process have been immensely increasing the precision of analysis, and at the same time decreasing the cost of research.<ref name="palace"/><br />
<br />
===Business===<br />
Data mining is commonly used in business <ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:bookitem&rft.title=Introduction+to+Data+Mining+and+its+Applications&rft.atitle=Data+Mining+in+Sales+Marketing+and+Finance&rft.creator=Sumathi%2C+S&rft.creator=Sivanandam%2C+S+N&rft.series=Studies+in+Computational+Intelligence&rft.date=2006-01-01&rft.pub=Springer+Berlin+Heidelberg&rft.isbn=9783540343509&rft.issn=1860-949X&rft.volume=29&rft.spage=411&rft.epage=438&rft_id=info:doi/10.1007%2F978-3-540-34351-6_18&rft.externalDBID=n%2Fa&rft.externalDocID=978-3-540-34351-6_133505_Chap18 Data Mining in Sales Marketing and Finance. (2006). Introduction to Data Mining and its Applications (Vol. 29, pp. 411-438). Berlin, Heidelberg: Springer Berlin Heidelberg.]</ref> to determine what demographics are buying what products, and to help firms predict customer preferences. Online retailers often track the ways which their clients interact with the content on their site. This leads to an extensive collection of data that can be used by data mining procedures to model representative "personas" of future clients who will visit the site. This has meaningful value for companies, because information generated from data mining practices can lead to more "click-throughs" and potentially, more "conversions" (sales).<br />
<br />
===Science/Academics===<br />
Data Mining also plays a crucial role in scientific analysis. The objective of many large research studies is to find patterns in experimental data. Many data mining procedures allow for applicable modeling of data in a way that is much faster and easier than doing so by hand.<br />
<br />
===Other Uses===<br />
Data Mining techniques have been used in a variety of other matters, such as in admissions processes in universities and political campaigns. <ref>http://communities.washingtontimes.com/neighborhood/political-potpourri/2012/nov/17/how-obama-won/</ref><br />
<ref>http://www.tnr.com/article/110862/data-mining-the-ineffective-future-affirmative-action-in-education#</ref> <br />
<br />
==Ethical Implications==<br />
Data mining is an inherently morally-neutral action, as it is simply the practice of working with large stored data sets and the process itself does not account for the way that the data was generated or will inevitably be used. The ethics of data mining come under scrutiny when the type of data being mined is something that the individuals or specific entities subject to the mining feel should not be disclosed for others to view. Issues also arise when sufficiently robust restrictions are not in place to hide the private characteristics of an individual’s personal identity in the online environment. Likewise, when firms neglect to take adequately tested precautionary measures for protecting a customer’s data as part of a larger collective set of consumer information, they are effectively opening all their clients’ data up to the possibility of unprotected digital consumption and exploitation.<br />
<br />
===User Profiling and Identification===<br />
In an attempt to build accurate models, data miners in online environments occasionally utilize the personal information of individuals who visit their sites. The information generally collected includes, but is not limited to, geographic identifiers, records of past behavior, or information that can identify an individual by a range of other intimate attributes like user account details or IP addresses. Many users do not actively realize that their information is being collected and many question the ethics of using an individual’s private information without their outright and formal consent.<ref>http://www.ecommercetimes.com/story/52616.html</ref> Additionally, data mining practices and tools have made the automated profiling of groups and individuals an easy task for a growing number of the technically literate. Using data mining to create customer profiling questions also raises ethical concerns regarding these practices due to the associated risks it creates for discrimination, de-individualization, and information asymmetries.<ref>Schermer, B.W. (2011). The limits of privacy in automated profiling and data mining. Computer Law and Security Report, 27(1), pp. 45-52.</ref><br />
<br />
===Distortion of Truth===<br />
Second to privacy concerns, distorting or misrepresenting trends in data that are not actually there together account for the other main ethical issue on the topic of data mining. The result of human or programmatic mishaps, generating flawed associations between data points can be severely costly to reputations, economic footings, and the continued confidence in the reliability of the data analysis method.<br />
<br />
===Anonymity and Ownership===<br />
Many information systems contain data on individuals, so maintaining anonymity of the data and informing those whose information is being used, exactly how it is used and to what effect, is not only important but in many cases a legally binding matter. Allowing individuals the freedom to opt in or opt out of data mining processes is one way to ensure some form of ethical responsibility.<br />
<br />
Discussion regarding the role of data ownership emerges as a natural consequence to its collection from disparate sources and its subsequent composite processing, analysis, and summarization. A contested point in some circumstances is the differentiation between raw source data (input) and the proprietary information product (output). Because the former, when in aggregate form, represents the constituent building blocks of the latter, the resulting interdependency spawns many debates surrounding who, if anyone, rightfully owns each intermediate information good and the finished analysis. Boiling down to where lines are drawn with respect to voluntary (or involuntary) data relinquishment and the perception of residual value, the ground is ripe for dispute and ethical ambiguity.<br />
<br />
===Use Case #1: Social Networking===<br />
Mining data found on social networking sites poses a particular challenge, as a great deal of personal information can be found on individuals’ social media pages. It is not uncommon for social media firms like Facebook and Twitter to sell the information mined from users’ personal pages to advertisers and academic researchers. Despite the fact that this data is anonymized and/or collected from a large pool of public-facing accounts, there are still a number of valid sources for apprehension regarding its execution and overall legitimacy.<br />
<br />
To reduce users’ qualms surrounding this type of activity and to ensure that the ethical validity of all such experimentation meets a sound moral standard, committees in the form of [[Wikipedia:Institutional_review_board|institutional review boards]] (IRBs) are organized across both industry and the social science disciplines of higher-learning. They are entrusted with the responsibility for reviewing, monitoring, and approving of any biomedical or behavioral research involving humans.<ref>Wikipedia: Institutional review board https://en.wikipedia.org/wiki/Institutional_review_board</ref> Because the natural extension of examining humans’ behavioral tendencies places us in the digital realm and thus satisfies the need for the aforementioned requisite oversight, here too IRBs serve a vital role in fulfilling the third-party ethical assessment of data collection and data manipulation for the sake of research driven advertising or academic inquiry.<br />
<br />
Firms often explicitly state in the terms and conditions of using their sites that they reserve the right to sell or use an individual's personally volunteered information toward serving their own business needs. Although this activity is controlled and kept within defined limits by consumer privacy laws, vigilant monitoring is necessary by the [[Wikipedia:Federal_Trade_Commission|Federal Trade Commission]] (FTC) and other such organizations to ensure that businesses are conducting themselves both fairly and lawfully. Because a user agreement is usually only displayed once (typically during the registration process), if it all, the gravity and seriousness ascribed to its contents is frequently undervalued by users, often relegated as unimportant and a low priority.<br />
<br />
As a majority of avid social media users are only concerned about a platform’s (superficially free) software and community assets -- expressed through its ability to cultivate social capital or the manner by which it advocates for a “more open and connected world” (e.g. Facebook) -- the embedded costs posed by data mining to users are subliminal and certainly not without their ramifications. Many users feel that the pursuit is an irresponsible practice, irrespective of the safeguards and stipulations found in user agreements, and consider it to be an activity in which social media firms should not engage.<ref>Facebook, [http://www.facebook.com/advertising/ Advertising]</ref><ref>http://mashable.com/2011/02/25/data-mining-social-marketing/</ref> Often for that reason, or purely on the rationale that the sum total of one’s social contributions could be aggregated and packaged up for sale at any time to an outside party -- without due notification or option for opting out -- many people who choose not to participate on Web-based social platforms due so for concerns that mirror those convictions. Increasing the transparency of what data is mined and identifying when a user is particularly vulnerable (i.e. for disregarding privacy measures) has the potential to restore the necessary confidence in weary users.<br />
<br />
===Use Case #2: Business===<br />
Much like the value that can be derived from mining social sites, a wide array of businesses have a similar opportunity in leveraging data mining techniques for improving their respective processes and ultimately, economic returns. Reducing expenses and maximizing profits is a shared challenge among for-profit institutions. As a result, it is not uncommon for corporations spanning various disciplines to use mined data in conjunction with analytic and predictive methods for getting a more informed business perspective. Ethically, many of the issues that confront businesses with respect to data mining are similar to other contextual use cases: they revolve around the notion of privacy and the extent to which the resulting information can be shared with third-parties. Because the benefits of such activity can be used to “...reduce fraud, anticipate resource demand, increase acquisition and curb customer attrition,” the attractive nature of the process generally outweighs any lingering moral uncertainties.<ref>SAS Enterprise Analytics https://www.sas.com/en_us/software/analytics/enterprise-miner.html</ref><br />
<br />
===Use Case #3: Government===<br />
Privacy is a theme that pertains to almost every possible application of data mining as an information revealing mechanism. The case happens to be no different when it is discussed in the frame of government utilization. There are hundreds of available uses, with some of the most recently relevant being in the realm of predicting and and preventing terrorism.<br />
<br />
===Use Case #4: Healthcare===<br />
As more information related to individuals’ health records becomes digitized, data mining will become significantly more useful within the healthcare field. Patients’ health records, while used in aggregate, will provide significant insight into causes of specific illnesses. The success of data mining in this context hinges on the process of converting significant amounts of data into standardized digital formats on software frameworks with low storage and search costs.<br />
<br />
==See also==<br />
* [[Data Aggregation Online]]<br />
* [[Targeted Advertising (Online)]]<br />
* [[Geographic Information Systems]]<br />
*[[Recommender Systems]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Concepts]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Data_Mining&diff=56929Data Mining2016-04-21T16:49:44Z<p>Zzasuwa: /* Use Case #1: Social Networking */</p>
<hr />
<div>[[File:DiagramDataMining.jpg|right|thumb|350px|This illustration shows the role [[Wikipedia:Data Mining|Data Mining]] plays in processing information for business use.]]<br />
'''Data Mining''' is the act of analyzing data from various perspectives and summarizing it into useful information, which combines aspects of artificial intelligence, machine learning, statistics and database systems. Software is implemented as one of many analytical tools used to analyze data. Through data mining, data is presented to users from many different angles, in various categories and relationships. On a more technical term, data mining is the process of realizing correlations or patterns among large fields of relative databases.<ref name="palace">[http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/index.htm Palace, Bill. "What Is Data Mining?" Data Mining. Anderson Graduate School of Management at UCLA, Mar. 1996. Web. 16 Dec. 2011.]</ref><br />
<br />
Data mining is used in many fields such as business, marketing, engineering, medicine, and the music and gaming industry. Businesses employ data mining techniques to gather information on their customers to better advertise and target future customers, while data mining is used to recognize patterns and designate categories for work in gaming, medicine, and music. Because personal information is gathered and studied, ethical concerns arise from the lack of anonymity and privacy of the subjects used in data mining techniques.<br />
<br />
<br />
==Process of Data Mining==<br />
The process of Data Mining requires the extraction of useful information from large sets of data stored in a uniform way, often in a database. The type of information extracted is relative to the type of data available, and the purpose of the data mining project itself – the information extracted could be modeled or represented of the entire set of data for example if the objective was to draw a conclusion of the dataset as a whole. <ref>http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf</ref><br />
<br />
Data mining consists of multiple sub-tasks:<ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Olaru, C., & Wehenkel, L. (1999). Data mining. IEEE Computer Applications in Power, 12(3), 19-25. doi:10.1109/67.773801]</ref><ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Shen Bin, Liu Yuan, & Wang Xiaoyi. (2010). Research on data mining models for the internet of things. 2010 International Conference on Image Analysis and Signal Processing (IASP) (pp. 127-132). Presented at the 2010 International Conference on Image Analysis and Signal Processing (IASP), IEEE. doi:10.1109/IASP.2010.5476146]</ref><br />
<br />
===Anomaly Detection=== <br />
Also called deviation detection, this is the “data cleaning” process where data miners attempt to identify unusual circumstances within the data that may belie anomalies or errors. The process includes program-aided searches of the data, parsing the data through filters that check for certain cases, and searching for unexplainable patterns within the data. The anomaly detection process often requires checking how the data was aggregated, and if human error had an effect on the data present.<br />
<br />
===Clustering=== <br />
[[File:Clustered.png|right|thumb|350px|A database of songs that has been visually represented as clusters of data which best represent the categories of songs present (ie. hip-hop, punk).]]<br />
Situations arise within datasets where there is a desire to know if similarities exist within the data, but no mechanism for detecting them exists. Clustering procedures within data mining aim to find similar objects within the data or to analyze correlations between attributes of the data. Clustering is also used to find topographical similarities within data sets.<br />
<br />
===Classification=== <br />
Use of mined data requires the input and output information to be formatted in a discrete way in order for the information to be useful. Classifying traits of both the input and output of the data minimizes ambiguity, and helps define unique qualities of the data, and the meaning of the data itself. This process also helps discover unique relationships that exist between the data present in the database (the input data), and information that is being presented as conclusive of the data set (the output data). Classification can occur in many formats, from labeling, to building models which fit the particular data set explicitly.<br />
<br />
===Regression=== <br />
The building of a transparent model which fits the data, and adequately conveys the information contained within the data set is the objective of the regression step of data mining. The use of statistical methods is the most common approach within the regression step, because it yields discrete output. Regression modeling also attempts to minimize error within the other steps of the data mining process.<br />
<br />
===Summarization===<br />
This task aims at producing representative descriptions of the data which provides context for the data. The summary process can take multiple formats, the most common of which is numerical analysis, which provides statistical quantification for patterns in the data like the mean or standard deviation found within the data set. These results are often represented in graphical formats – such as histograms or scatter plots. Qualitative data can be summarized by giving a list of trends or frequencies within a set of data. These results are useful because they represent the entirety of the data set in a much more digestible format.<br />
<br />
==Data Mining Tools==<br />
<br />
As data mining is becoming more popular to sift through the massive amounts of data available online, new tools have been cropping up to make the job simpler. Google, for example, has released a free tool called Correlate<ref>http://www.fastcompany.com/1755287/google-correlate-tool-gives-marketers-powerful-new-data-mining-tools</ref> aimed at helping corporations utilize the Google search database to draw conclusions on consumer behavior. MIT and Harvard have recently collaborated in the creation of MINE, a tool of unprecedented power which can analyze more data for more complex patterns than any tool previously available. It is said to approach the quality of human examination in terms of being able to pick up on nonstandard or deceptive patterns<ref>http://www.decodedscience.com/data-mining-tool-advances-mine-ranks-multiple-patterns/7959</ref>.<br />
<br />
==Applications of Data Mining==<br />
===Examples===<br />
A simple example of data mining is analyzing a large population, such as University of Michigan students, and determining simple characteristics that the data has, such as the proportion of the student body that is from each ethnic background.<br />
<br />
Before the term "data mining" came into popular use, many businesses had already implemented its technology. They used powerful computers to comb through quantitative data from supermarket scanners, and analyzed the resulting data for market research purposes. This process have been immensely increasing the precision of analysis, and at the same time decreasing the cost of research.<ref name="palace"/><br />
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===Business===<br />
Data mining is commonly used in business <ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:bookitem&rft.title=Introduction+to+Data+Mining+and+its+Applications&rft.atitle=Data+Mining+in+Sales+Marketing+and+Finance&rft.creator=Sumathi%2C+S&rft.creator=Sivanandam%2C+S+N&rft.series=Studies+in+Computational+Intelligence&rft.date=2006-01-01&rft.pub=Springer+Berlin+Heidelberg&rft.isbn=9783540343509&rft.issn=1860-949X&rft.volume=29&rft.spage=411&rft.epage=438&rft_id=info:doi/10.1007%2F978-3-540-34351-6_18&rft.externalDBID=n%2Fa&rft.externalDocID=978-3-540-34351-6_133505_Chap18 Data Mining in Sales Marketing and Finance. (2006). Introduction to Data Mining and its Applications (Vol. 29, pp. 411-438). Berlin, Heidelberg: Springer Berlin Heidelberg.]</ref> to determine what demographics are buying what products, and to help firms predict customer preferences. Online retailers often track the ways which their clients interact with the content on their site. This leads to an extensive collection of data that can be used by data mining procedures to model representative "personas" of future clients who will visit the site. This has meaningful value for companies, because information generated from data mining practices can lead to more "click-throughs" and potentially, more "conversions" (sales).<br />
<br />
===Science/Academics===<br />
Data Mining also plays a crucial role in scientific analysis. The objective of many large research studies is to find patterns in experimental data. Many data mining procedures allow for applicable modeling of data in a way that is much faster and easier than doing so by hand.<br />
<br />
===Other Uses===<br />
Data Mining techniques have been used in a variety of other matters, such as in admissions processes in universities and political campaigns. <ref>http://communities.washingtontimes.com/neighborhood/political-potpourri/2012/nov/17/how-obama-won/</ref><br />
<ref>http://www.tnr.com/article/110862/data-mining-the-ineffective-future-affirmative-action-in-education#</ref> <br />
<br />
==Ethical Implications==<br />
Data mining is an inherently morally-neutral action, as it is simply the practice of working with large stored data sets and the process itself does not account for the way that the data was generated or will inevitably be used. The ethics of data mining come under scrutiny when the type of data being mined is something that the individuals or specific entities subject to the mining feel should not be disclosed for others to view. Issues also arise when sufficiently robust restrictions are not in place to hide the private characteristics of an individual’s personal identity in the online environment. Likewise, when firms neglect to take adequately tested precautionary measures for protecting a customer’s data as part of a larger collective set of consumer information, they are effectively opening all their clients’ data up to the possibility of unprotected digital consumption and exploitation.<br />
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===User Profiling and Identification===<br />
In an attempt to build accurate models, data miners in online environments occasionally utilize the personal information of individuals who visit their sites. The information generally collected includes, but is not limited to, geographic identifiers, records of past behavior, or information that can identify an individual by a range of other intimate attributes like user account details or IP addresses. Many users do not actively realize that their information is being collected and many question the ethics of using an individual’s private information without their outright and formal consent.<ref>http://www.ecommercetimes.com/story/52616.html</ref> Additionally, data mining practices and tools have made the automated profiling of groups and individuals an easy task for a growing number of the technically literate. Using data mining to create customer profiling questions also raises ethical concerns regarding these practices due to the associated risks it creates for discrimination, de-individualization, and information asymmetries.<ref>Schermer, B.W. (2011). The limits of privacy in automated profiling and data mining. Computer Law and Security Report, 27(1), pp. 45-52.</ref><br />
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===Distortion of Truth===<br />
Second to privacy concerns, distorting or misrepresenting trends in data that are not actually there together account for the other main ethical issue on the topic of data mining. The result of human or programmatic mishaps, generating flawed associations between data points can be severely costly to reputations, economic footings, and the continued confidence in the reliability of the data analysis method.<br />
<br />
===Anonymity and Ownership===<br />
Many information systems contain data on individuals, so maintaining anonymity of the data and informing those whose information is being used, exactly how it is used and to what effect, is not only important but in many cases a legally binding matter. Allowing individuals the freedom to opt in or opt out of data mining processes is one way to ensure some form of ethical responsibility.<br />
<br />
Discussion regarding the role of data ownership emerges as a natural consequence to its collection from disparate sources and its subsequent composite processing, analysis, and summarization. A contested point in some circumstances is the differentiation between raw source data (input) and the proprietary information product (output). Because the former, when in aggregate form, represents the constituent building blocks of the latter, the resulting interdependency spawns many debates surrounding who, if anyone, rightfully owns each intermediate information good and the finished analysis. Boiling down to where lines are drawn with respect to voluntary (or involuntary) data relinquishment and the perception of residual value, the ground is ripe for dispute and ethical ambiguity.<br />
<br />
===Use Case #1: Social Networking===<br />
Mining data found on social networking sites poses a particular challenge, as a great deal of personal information can be found on individuals’ social media pages. It is not uncommon for social media firms like Facebook and Twitter to sell the information mined from users’ personal pages to advertisers and academic researchers. Despite the fact that this data is anonymized and/or collected from a large pool of public-facing accounts, there are still a number of valid sources for apprehension regarding its execution and overall legitimacy.<br />
<br />
To reduce users’ qualms surrounding this type of activity and to ensure that the ethical validity of all such experimentation meets a sound moral standard, committees in the form of [[Wikipedia:Institutional_review_board|institutional review boards]] (IRBs) are organized across both industry and the social science disciplines of higher-learning. They are entrusted with the responsibility for reviewing, monitoring, and approving of any biomedical or behavioral research involving humans.<ref>Wikipedia: Institutional review board https://en.wikipedia.org/wiki/Institutional_review_board</ref> Because the natural extension of examining humans’ behavioral tendencies places us in the digital realm and thus satisfies the need for the aforementioned requisite oversight, here too IRBs serve a vital role in fulfilling the third-party ethical assessment of data collection and data manipulation for the sake of research driven advertising or academic inquiry.<br />
<br />
Firms often explicitly state in the terms and conditions of using their sites that they reserve the right to sell or use an individual's personally volunteered information toward serving their own business needs. Although this activity is controlled and kept within defined limits by consumer privacy laws, vigilant monitoring is necessary by the [[Wikipedia:Federal_Trade_Commission|Federal Trade Commission]] (FTC) and other such organizations to ensure that businesses are conducting themselves both fairly and lawfully. Because a user agreement is usually only displayed once (typically during the registration process), if it all, the gravity and seriousness ascribed to its contents is frequently undervalued by users, often relegated as unimportant and a low priority.<br />
<br />
As a majority of avid social media users are only concerned about a platform’s (superficially free) software and community assets -- expressed through its ability to cultivate social capital or the manner by which it advocates for a “more open and connected world” (e.g. Facebook) -- the embedded costs posed by data mining to users are subliminal and certainly not without their ramifications. Many users feel that the pursuit is an irresponsible practice, irrespective of the safeguards and stipulations found in user agreements, and consider it to be an activity in which social media firms should not engage.<ref>Facebook, [http://www.facebook.com/advertising/ Advertising]</ref><ref>http://mashable.com/2011/02/25/data-mining-social-marketing/</ref> Often for that reason, or purely on the rationale that the sum total of one’s social contributions could be aggregated and packaged up for sale at any time to an outside party -- without due notification or option for opting out -- many people who choose not to participate on Web-based social platforms due so for concerns that mirror those convictions. Increasing the transparency of what data is mined and identifying when a user is particularly vulnerable (i.e. for disregarding privacy measures) has the potential to restore the necessary confidence in weary users.<br />
<br />
===Use Case #2: Business===<br />
Much like the value that can be derived from mining social sites, a wide array of businesses has a similar opportunity in leveraging data mining techniques for improving their respective processes and ultimately, economic returns. Reducing expenses and maximizing profits is a shared challenge among all for-profit institutions. As a result, it is not uncommon for corporations spanning various disciplines to use mined data in conjunction with analytic and predictive methods for getting a more informed business perspective. Ethically, many of the issues that confront businesses with respect to data mining are similar to other contextual use cases: they revolve around the notion of privacy and the extent to which the resulting information can be shared with third-parties. Because the benefits of such activity can be used to “...reduce fraud, anticipate resource demand, increase acquisition and curb customer attrition,” the attractive nature of the process generally outweighs any lingering moral uncertainties.<ref>SAS Enterprise Analytics https://www.sas.com/en_us/software/analytics/enterprise-miner.html</ref><br />
<br />
===Use Case #3: Government===<br />
Privacy is a theme that pertains to almost every possible application of data mining as an information revealing mechanism. The case happens to be no different when it is discussed in the frame of government utilization. There are hundreds of available uses, with some of the most recently relevant being in the realm of predicting and and preventing terrorism.<br />
<br />
===Use Case #4: Healthcare===<br />
As more information related to individuals’ health records becomes digitized, data mining will become significantly more useful within the healthcare field. Patients’ health records, while used in aggregate, will provide significant insight into causes of specific illnesses. The success of data mining in this context hinges on the process of converting significant amounts of data into standardized digital formats on software frameworks with low storage and search costs.<br />
<br />
==See also==<br />
* [[Data Aggregation Online]]<br />
* [[Targeted Advertising (Online)]]<br />
* [[Geographic Information Systems]]<br />
*[[Recommender Systems]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Concepts]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Data_Mining&diff=56927Data Mining2016-04-21T16:39:03Z<p>Zzasuwa: /* Use Case #1: Social Networking */</p>
<hr />
<div>[[File:DiagramDataMining.jpg|right|thumb|350px|This illustration shows the role [[Wikipedia:Data Mining|Data Mining]] plays in processing information for business use.]]<br />
'''Data Mining''' is the act of analyzing data from various perspectives and summarizing it into useful information, which combines aspects of artificial intelligence, machine learning, statistics and database systems. Software is implemented as one of many analytical tools used to analyze data. Through data mining, data is presented to users from many different angles, in various categories and relationships. On a more technical term, data mining is the process of realizing correlations or patterns among large fields of relative databases.<ref name="palace">[http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/index.htm Palace, Bill. "What Is Data Mining?" Data Mining. Anderson Graduate School of Management at UCLA, Mar. 1996. Web. 16 Dec. 2011.]</ref><br />
<br />
Data mining is used in many fields such as business, marketing, engineering, medicine, and the music and gaming industry. Businesses employ data mining techniques to gather information on their customers to better advertise and target future customers, while data mining is used to recognize patterns and designate categories for work in gaming, medicine, and music. Because personal information is gathered and studied, ethical concerns arise from the lack of anonymity and privacy of the subjects used in data mining techniques.<br />
<br />
<br />
==Process of Data Mining==<br />
The process of Data Mining requires the extraction of useful information from large sets of data stored in a uniform way, often in a database. The type of information extracted is relative to the type of data available, and the purpose of the data mining project itself – the information extracted could be modeled or represented of the entire set of data for example if the objective was to draw a conclusion of the dataset as a whole. <ref>http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf</ref><br />
<br />
Data mining consists of multiple sub-tasks:<ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Olaru, C., & Wehenkel, L. (1999). Data mining. IEEE Computer Applications in Power, 12(3), 19-25. doi:10.1109/67.773801]</ref><ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Shen Bin, Liu Yuan, & Wang Xiaoyi. (2010). Research on data mining models for the internet of things. 2010 International Conference on Image Analysis and Signal Processing (IASP) (pp. 127-132). Presented at the 2010 International Conference on Image Analysis and Signal Processing (IASP), IEEE. doi:10.1109/IASP.2010.5476146]</ref><br />
<br />
===Anomaly Detection=== <br />
Also called deviation detection, this is the “data cleaning” process where data miners attempt to identify unusual circumstances within the data that may belie anomalies or errors. The process includes program-aided searches of the data, parsing the data through filters that check for certain cases, and searching for unexplainable patterns within the data. The anomaly detection process often requires checking how the data was aggregated, and if human error had an effect on the data present.<br />
<br />
===Clustering=== <br />
[[File:Clustered.png|right|thumb|350px|A database of songs that has been visually represented as clusters of data which best represent the categories of songs present (ie. hip-hop, punk).]]<br />
Situations arise within datasets where there is a desire to know if similarities exist within the data, but no mechanism for detecting them exists. Clustering procedures within data mining aim to find similar objects within the data or to analyze correlations between attributes of the data. Clustering is also used to find topographical similarities within data sets.<br />
<br />
===Classification=== <br />
Use of mined data requires the input and output information to be formatted in a discrete way in order for the information to be useful. Classifying traits of both the input and output of the data minimizes ambiguity, and helps define unique qualities of the data, and the meaning of the data itself. This process also helps discover unique relationships that exist between the data present in the database (the input data), and information that is being presented as conclusive of the data set (the output data). Classification can occur in many formats, from labeling, to building models which fit the particular data set explicitly.<br />
<br />
===Regression=== <br />
The building of a transparent model which fits the data, and adequately conveys the information contained within the data set is the objective of the regression step of data mining. The use of statistical methods is the most common approach within the regression step, because it yields discrete output. Regression modeling also attempts to minimize error within the other steps of the data mining process.<br />
<br />
===Summarization===<br />
This task aims at producing representative descriptions of the data which provides context for the data. The summary process can take multiple formats, the most common of which is numerical analysis, which provides statistical quantification for patterns in the data like the mean or standard deviation found within the data set. These results are often represented in graphical formats – such as histograms or scatter plots. Qualitative data can be summarized by giving a list of trends or frequencies within a set of data. These results are useful because they represent the entirety of the data set in a much more digestible format.<br />
<br />
==Data Mining Tools==<br />
<br />
As data mining is becoming more popular to sift through the massive amounts of data available online, new tools have been cropping up to make the job simpler. Google, for example, has released a free tool called Correlate<ref>http://www.fastcompany.com/1755287/google-correlate-tool-gives-marketers-powerful-new-data-mining-tools</ref> aimed at helping corporations utilize the Google search database to draw conclusions on consumer behavior. MIT and Harvard have recently collaborated in the creation of MINE, a tool of unprecedented power which can analyze more data for more complex patterns than any tool previously available. It is said to approach the quality of human examination in terms of being able to pick up on nonstandard or deceptive patterns<ref>http://www.decodedscience.com/data-mining-tool-advances-mine-ranks-multiple-patterns/7959</ref>.<br />
<br />
==Applications of Data Mining==<br />
===Examples===<br />
A simple example of data mining is analyzing a large population, such as University of Michigan students, and determining simple characteristics that the data has, such as the proportion of the student body that is from each ethnic background.<br />
<br />
Before the term "data mining" came into popular use, many businesses had already implemented its technology. They used powerful computers to comb through quantitative data from supermarket scanners, and analyzed the resulting data for market research purposes. This process have been immensely increasing the precision of analysis, and at the same time decreasing the cost of research.<ref name="palace"/><br />
<br />
===Business===<br />
Data mining is commonly used in business <ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:bookitem&rft.title=Introduction+to+Data+Mining+and+its+Applications&rft.atitle=Data+Mining+in+Sales+Marketing+and+Finance&rft.creator=Sumathi%2C+S&rft.creator=Sivanandam%2C+S+N&rft.series=Studies+in+Computational+Intelligence&rft.date=2006-01-01&rft.pub=Springer+Berlin+Heidelberg&rft.isbn=9783540343509&rft.issn=1860-949X&rft.volume=29&rft.spage=411&rft.epage=438&rft_id=info:doi/10.1007%2F978-3-540-34351-6_18&rft.externalDBID=n%2Fa&rft.externalDocID=978-3-540-34351-6_133505_Chap18 Data Mining in Sales Marketing and Finance. (2006). Introduction to Data Mining and its Applications (Vol. 29, pp. 411-438). Berlin, Heidelberg: Springer Berlin Heidelberg.]</ref> to determine what demographics are buying what products, and to help firms predict customer preferences. Online retailers often track the ways which their clients interact with the content on their site. This leads to an extensive collection of data that can be used by data mining procedures to model representative "personas" of future clients who will visit the site. This has meaningful value for companies, because information generated from data mining practices can lead to more "click-throughs" and potentially, more "conversions" (sales).<br />
<br />
===Science/Academics===<br />
Data Mining also plays a crucial role in scientific analysis. The objective of many large research studies is to find patterns in experimental data. Many data mining procedures allow for applicable modeling of data in a way that is much faster and easier than doing so by hand.<br />
<br />
===Other Uses===<br />
Data Mining techniques have been used in a variety of other matters, such as in admissions processes in universities and political campaigns. <ref>http://communities.washingtontimes.com/neighborhood/political-potpourri/2012/nov/17/how-obama-won/</ref><br />
<ref>http://www.tnr.com/article/110862/data-mining-the-ineffective-future-affirmative-action-in-education#</ref> <br />
<br />
==Ethical Implications==<br />
Data mining is an inherently morally-neutral action, as it is simply the practice of working with large stored data sets and the process itself does not account for the way that the data was generated or will inevitably be used. The ethics of data mining come under scrutiny when the type of data being mined is something that the individuals or specific entities subject to the mining feel should not be disclosed for others to view. Issues also arise when sufficiently robust restrictions are not in place to hide the private characteristics of an individual’s personal identity in the online environment. Likewise, when firms neglect to take adequately tested precautionary measures for protecting a customer’s data as part of a larger collective set of consumer information, they are effectively opening all their clients’ data up to the possibility of unprotected digital consumption and exploitation.<br />
<br />
===User Profiling and Identification===<br />
In an attempt to build accurate models, data miners in online environments occasionally utilize the personal information of individuals who visit their sites. The information generally collected includes, but is not limited to, geographic identifiers, records of past behavior, or information that can identify an individual by a range of other intimate attributes like user account details or IP addresses. Many users do not actively realize that their information is being collected and many question the ethics of using an individual’s private information without their outright and formal consent.<ref>http://www.ecommercetimes.com/story/52616.html</ref> Additionally, data mining practices and tools have made the automated profiling of groups and individuals an easy task for a growing number of the technically literate. Using data mining to create customer profiling questions also raises ethical concerns regarding these practices due to the associated risks it creates for discrimination, de-individualization, and information asymmetries.<ref>Schermer, B.W. (2011). The limits of privacy in automated profiling and data mining. Computer Law and Security Report, 27(1), pp. 45-52.</ref><br />
<br />
===Distortion of Truth===<br />
Second to privacy concerns, distorting or misrepresenting trends in data that are not actually there together account for the other main ethical issue on the topic of data mining. The result of human or programmatic mishaps, generating flawed associations between data points can be severely costly to reputations, economic footings, and the continued confidence in the reliability of the data analysis method.<br />
<br />
===Anonymity and Ownership===<br />
Many information systems contain data on individuals, so maintaining anonymity of the data and informing those whose information is being used, exactly how it is used and to what effect, is not only important but in many cases a legally binding matter. Allowing individuals the freedom to opt in or opt out of data mining processes is one way to ensure some form of ethical responsibility.<br />
<br />
Discussion regarding the role of data ownership emerges as a natural consequence to its collection from disparate sources and its subsequent composite processing, analysis, and summarization. A contested point in some circumstances is the differentiation between raw source data (input) and the proprietary information product (output). Because the former, when in aggregate form, represents the constituent building blocks of the latter, the resulting interdependency spawns many debates surrounding who, if anyone, rightfully owns each intermediate information good and the finished analysis. Boiling down to where lines are drawn with respect to voluntary (or involuntary) data relinquishment and the perception of residual value, the ground is ripe for dispute and ethical ambiguity.<br />
<br />
===Use Case #1: Social Networking===<br />
Mining data found on social networking sites poses a particular challenge, as a great deal of personal information can be found on individuals’ social media pages. It is not uncommon for social media firms like Facebook and Twitter to sell the information mined from users’ personal pages to advertisers and academic researchers. Despite the fact that this data is anonymized and/or collected from a large pool of public-facing accounts, there are still a number of valid sources for apprehension regarding its execution and overall legitimacy.<br />
<br />
To reduce users’ qualms surrounding this type of activity and to ensure that the ethical validity of all such experimentation meets a sound moral standard, committees in the form of [[Wikipedia:Institutional_review_board|institutional review boards]] (IRBs) are organized across both industry and the social science disciplines of higher-learning. They are entrusted with the responsibility for reviewing, monitoring, and approving of any biomedical or behavioral research involving humans.<ref>Wikipedia: Institutional review board https://en.wikipedia.org/wiki/Institutional_review_board</ref> Because the natural extension of examining humans’ behavioral tendencies places us in the digital realm and thus satisfies the need for the aforementioned requisite oversight, here too IRBs serve a vital role in fulfilling the third-party ethical assessment of data collection and data manipulation for the sake of research driven advertising or academic inquiry.<br />
<br />
Firms often explicitly state in the terms and conditions of using their sites that they reserve the right to sell or use an individual's personally volunteered information toward serving their own business needs. Although this activity is controlled and kept within defined limits by consumer privacy laws, vigilant monitoring is necessary by the [[Wikipedia:Federal_Trade_Commission|Federal Trade Commission]] (FTC) and other such organizations to ensure that businesses are conducting themselves both fairly and lawfully. Because a user agreement is usually only displayed once (typically during the registration process), if it all, the gravity and seriousness ascribed to its contents is frequently undervalued by users, often relegated as unimportant and a low priority.<br />
<br />
As a majority of avid social media users are only concerned about a platform’s (superficially free) software and community assets -- expressed through its ability to cultivate social capital or the manner by which it advocates for a “more open and connected world” (e.g. Facebook) -- the embedded costs posed by data mining to users are subliminal and certainly not without their ramifications. Many users feel that the pursuit is an irresponsible practice, irrespective of the safeguards and stipulations found in user agreements, and consider it to be an activity in which social media firms should not engage.<ref>Facebook, [http://www.facebook.com/advertising/ Advertising]</ref><ref>http://mashable.com/2011/02/25/data-mining-social-marketing/</ref> Often for that reason, or purely on the rationale that the sum total of one’s social contributions could be aggregated and packaged up for sale at any time to an outside party, without due notification or option for opting out, many people who choose not to participate on Web-based social platforms due so for concerns that mirror those convictions. Increasing the transparency of what data is mined and whether a user is particularly vulnerable (i.e. for disregarding their privacy measures) has the potential to restore the necessary confidence in weary users for returning to the platform.<br />
<br />
===Use Case #2: Business===<br />
Much like the value that can be derived from mining social sites, a wide array of businesses has a similar opportunity in leveraging data mining techniques for improving their respective processes and ultimately, economic returns. Reducing expenses and maximizing profits is a shared challenge among all for-profit institutions. As a result, it is not uncommon for corporations spanning various disciplines to use mined data in conjunction with analytic and predictive methods for getting a more informed business perspective. Ethically, many of the issues that confront businesses with respect to data mining are similar to other contextual use cases: they revolve around the notion of privacy and the extent to which the resulting information can be shared with third-parties. Because the benefits of such activity can be used to “...reduce fraud, anticipate resource demand, increase acquisition and curb customer attrition,” the attractive nature of the process generally outweighs any lingering moral uncertainties.<ref>SAS Enterprise Analytics https://www.sas.com/en_us/software/analytics/enterprise-miner.html</ref><br />
<br />
===Use Case #3: Government===<br />
Privacy is a theme that pertains to almost every possible application of data mining as an information revealing mechanism. The case happens to be no different when it is discussed in the frame of government utilization. There are hundreds of available uses, with some of the most recently relevant being in the realm of predicting and and preventing terrorism.<br />
<br />
===Use Case #4: Healthcare===<br />
As more information related to individuals’ health records becomes digitized, data mining will become significantly more useful within the healthcare field. Patients’ health records, while used in aggregate, will provide significant insight into causes of specific illnesses. The success of data mining in this context hinges on the process of converting significant amounts of data into standardized digital formats on software frameworks with low storage and search costs.<br />
<br />
==See also==<br />
* [[Data Aggregation Online]]<br />
* [[Targeted Advertising (Online)]]<br />
* [[Geographic Information Systems]]<br />
*[[Recommender Systems]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Concepts]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Data_Mining&diff=56922Data Mining2016-04-21T16:31:17Z<p>Zzasuwa: /* Anonymity and Ownership */</p>
<hr />
<div>[[File:DiagramDataMining.jpg|right|thumb|350px|This illustration shows the role [[Wikipedia:Data Mining|Data Mining]] plays in processing information for business use.]]<br />
'''Data Mining''' is the act of analyzing data from various perspectives and summarizing it into useful information, which combines aspects of artificial intelligence, machine learning, statistics and database systems. Software is implemented as one of many analytical tools used to analyze data. Through data mining, data is presented to users from many different angles, in various categories and relationships. On a more technical term, data mining is the process of realizing correlations or patterns among large fields of relative databases.<ref name="palace">[http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/index.htm Palace, Bill. "What Is Data Mining?" Data Mining. Anderson Graduate School of Management at UCLA, Mar. 1996. Web. 16 Dec. 2011.]</ref><br />
<br />
Data mining is used in many fields such as business, marketing, engineering, medicine, and the music and gaming industry. Businesses employ data mining techniques to gather information on their customers to better advertise and target future customers, while data mining is used to recognize patterns and designate categories for work in gaming, medicine, and music. Because personal information is gathered and studied, ethical concerns arise from the lack of anonymity and privacy of the subjects used in data mining techniques.<br />
<br />
<br />
==Process of Data Mining==<br />
The process of Data Mining requires the extraction of useful information from large sets of data stored in a uniform way, often in a database. The type of information extracted is relative to the type of data available, and the purpose of the data mining project itself – the information extracted could be modeled or represented of the entire set of data for example if the objective was to draw a conclusion of the dataset as a whole. <ref>http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf</ref><br />
<br />
Data mining consists of multiple sub-tasks:<ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Olaru, C., & Wehenkel, L. (1999). Data mining. IEEE Computer Applications in Power, 12(3), 19-25. doi:10.1109/67.773801]</ref><ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Shen Bin, Liu Yuan, & Wang Xiaoyi. (2010). Research on data mining models for the internet of things. 2010 International Conference on Image Analysis and Signal Processing (IASP) (pp. 127-132). Presented at the 2010 International Conference on Image Analysis and Signal Processing (IASP), IEEE. doi:10.1109/IASP.2010.5476146]</ref><br />
<br />
===Anomaly Detection=== <br />
Also called deviation detection, this is the “data cleaning” process where data miners attempt to identify unusual circumstances within the data that may belie anomalies or errors. The process includes program-aided searches of the data, parsing the data through filters that check for certain cases, and searching for unexplainable patterns within the data. The anomaly detection process often requires checking how the data was aggregated, and if human error had an effect on the data present.<br />
<br />
===Clustering=== <br />
[[File:Clustered.png|right|thumb|350px|A database of songs that has been visually represented as clusters of data which best represent the categories of songs present (ie. hip-hop, punk).]]<br />
Situations arise within datasets where there is a desire to know if similarities exist within the data, but no mechanism for detecting them exists. Clustering procedures within data mining aim to find similar objects within the data or to analyze correlations between attributes of the data. Clustering is also used to find topographical similarities within data sets.<br />
<br />
===Classification=== <br />
Use of mined data requires the input and output information to be formatted in a discrete way in order for the information to be useful. Classifying traits of both the input and output of the data minimizes ambiguity, and helps define unique qualities of the data, and the meaning of the data itself. This process also helps discover unique relationships that exist between the data present in the database (the input data), and information that is being presented as conclusive of the data set (the output data). Classification can occur in many formats, from labeling, to building models which fit the particular data set explicitly.<br />
<br />
===Regression=== <br />
The building of a transparent model which fits the data, and adequately conveys the information contained within the data set is the objective of the regression step of data mining. The use of statistical methods is the most common approach within the regression step, because it yields discrete output. Regression modeling also attempts to minimize error within the other steps of the data mining process.<br />
<br />
===Summarization===<br />
This task aims at producing representative descriptions of the data which provides context for the data. The summary process can take multiple formats, the most common of which is numerical analysis, which provides statistical quantification for patterns in the data like the mean or standard deviation found within the data set. These results are often represented in graphical formats – such as histograms or scatter plots. Qualitative data can be summarized by giving a list of trends or frequencies within a set of data. These results are useful because they represent the entirety of the data set in a much more digestible format.<br />
<br />
==Data Mining Tools==<br />
<br />
As data mining is becoming more popular to sift through the massive amounts of data available online, new tools have been cropping up to make the job simpler. Google, for example, has released a free tool called Correlate<ref>http://www.fastcompany.com/1755287/google-correlate-tool-gives-marketers-powerful-new-data-mining-tools</ref> aimed at helping corporations utilize the Google search database to draw conclusions on consumer behavior. MIT and Harvard have recently collaborated in the creation of MINE, a tool of unprecedented power which can analyze more data for more complex patterns than any tool previously available. It is said to approach the quality of human examination in terms of being able to pick up on nonstandard or deceptive patterns<ref>http://www.decodedscience.com/data-mining-tool-advances-mine-ranks-multiple-patterns/7959</ref>.<br />
<br />
==Applications of Data Mining==<br />
===Examples===<br />
A simple example of data mining is analyzing a large population, such as University of Michigan students, and determining simple characteristics that the data has, such as the proportion of the student body that is from each ethnic background.<br />
<br />
Before the term "data mining" came into popular use, many businesses had already implemented its technology. They used powerful computers to comb through quantitative data from supermarket scanners, and analyzed the resulting data for market research purposes. This process have been immensely increasing the precision of analysis, and at the same time decreasing the cost of research.<ref name="palace"/><br />
<br />
===Business===<br />
Data mining is commonly used in business <ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:bookitem&rft.title=Introduction+to+Data+Mining+and+its+Applications&rft.atitle=Data+Mining+in+Sales+Marketing+and+Finance&rft.creator=Sumathi%2C+S&rft.creator=Sivanandam%2C+S+N&rft.series=Studies+in+Computational+Intelligence&rft.date=2006-01-01&rft.pub=Springer+Berlin+Heidelberg&rft.isbn=9783540343509&rft.issn=1860-949X&rft.volume=29&rft.spage=411&rft.epage=438&rft_id=info:doi/10.1007%2F978-3-540-34351-6_18&rft.externalDBID=n%2Fa&rft.externalDocID=978-3-540-34351-6_133505_Chap18 Data Mining in Sales Marketing and Finance. (2006). Introduction to Data Mining and its Applications (Vol. 29, pp. 411-438). Berlin, Heidelberg: Springer Berlin Heidelberg.]</ref> to determine what demographics are buying what products, and to help firms predict customer preferences. Online retailers often track the ways which their clients interact with the content on their site. This leads to an extensive collection of data that can be used by data mining procedures to model representative "personas" of future clients who will visit the site. This has meaningful value for companies, because information generated from data mining practices can lead to more "click-throughs" and potentially, more "conversions" (sales).<br />
<br />
===Science/Academics===<br />
Data Mining also plays a crucial role in scientific analysis. The objective of many large research studies is to find patterns in experimental data. Many data mining procedures allow for applicable modeling of data in a way that is much faster and easier than doing so by hand.<br />
<br />
===Other Uses===<br />
Data Mining techniques have been used in a variety of other matters, such as in admissions processes in universities and political campaigns. <ref>http://communities.washingtontimes.com/neighborhood/political-potpourri/2012/nov/17/how-obama-won/</ref><br />
<ref>http://www.tnr.com/article/110862/data-mining-the-ineffective-future-affirmative-action-in-education#</ref> <br />
<br />
==Ethical Implications==<br />
Data mining is an inherently morally-neutral action, as it is simply the practice of working with large stored data sets and the process itself does not account for the way that the data was generated or will inevitably be used. The ethics of data mining come under scrutiny when the type of data being mined is something that the individuals or specific entities subject to the mining feel should not be disclosed for others to view. Issues also arise when sufficiently robust restrictions are not in place to hide the private characteristics of an individual’s personal identity in the online environment. Likewise, when firms neglect to take adequately tested precautionary measures for protecting a customer’s data as part of a larger collective set of consumer information, they are effectively opening all their clients’ data up to the possibility of unprotected digital consumption and exploitation.<br />
<br />
===User Profiling and Identification===<br />
In an attempt to build accurate models, data miners in online environments occasionally utilize the personal information of individuals who visit their sites. The information generally collected includes, but is not limited to, geographic identifiers, records of past behavior, or information that can identify an individual by a range of other intimate attributes like user account details or IP addresses. Many users do not actively realize that their information is being collected and many question the ethics of using an individual’s private information without their outright and formal consent.<ref>http://www.ecommercetimes.com/story/52616.html</ref> Additionally, data mining practices and tools have made the automated profiling of groups and individuals an easy task for a growing number of the technically literate. Using data mining to create customer profiling questions also raises ethical concerns regarding these practices due to the associated risks it creates for discrimination, de-individualization, and information asymmetries.<ref>Schermer, B.W. (2011). The limits of privacy in automated profiling and data mining. Computer Law and Security Report, 27(1), pp. 45-52.</ref><br />
<br />
===Distortion of Truth===<br />
Second to privacy concerns, distorting or misrepresenting trends in data that are not actually there together account for the other main ethical issue on the topic of data mining. The result of human or programmatic mishaps, generating flawed associations between data points can be severely costly to reputations, economic footings, and the continued confidence in the reliability of the data analysis method.<br />
<br />
===Anonymity and Ownership===<br />
Many information systems contain data on individuals, so maintaining anonymity of the data and informing those whose information is being used, exactly how it is used and to what effect, is not only important but in many cases a legally binding matter. Allowing individuals the freedom to opt in or opt out of data mining processes is one way to ensure some form of ethical responsibility.<br />
<br />
Discussion regarding the role of data ownership emerges as a natural consequence to its collection from disparate sources and its subsequent composite processing, analysis, and summarization. A contested point in some circumstances is the differentiation between raw source data (input) and the proprietary information product (output). Because the former, when in aggregate form, represents the constituent building blocks of the latter, the resulting interdependency spawns many debates surrounding who, if anyone, rightfully owns each intermediate information good and the finished analysis. Boiling down to where lines are drawn with respect to voluntary (or involuntary) data relinquishment and the perception of residual value, the ground is ripe for dispute and ethical ambiguity.<br />
<br />
===Use Case #1: Social Networking===<br />
Mining data found on social networking sites poses a particular challenge, as a great deal of personal information can be found on individuals’ social media pages. It is not uncommon for social media firms like Facebook and Twitter to sell the information mined from users’ personal pages to advertisers and academic researchers. Despite the fact that this data is anonymized and/or collected from a large pool of public-facing accounts, there are still a number of valid sources for apprehension regarding its execution and overall legitimacy.<br />
<br />
To reduce users’ qualms surrounding this type of activity and to ensure that the ethical validity of all such experimentation meets a sound moral standard, committees in the form of [[Wikipedia:Institutional_review_board|institutional review boards]] (IRBs) are organized across both industry and the social science disciplines of higher-learning. They are entrusted with the responsibility for reviewing, monitoring, and approving of any biomedical or behavioral research involving humans.<ref>Wikipedia: Institutional review board https://en.wikipedia.org/wiki/Institutional_review_board</ref> Because the natural extension of examining humans’ behavioral tendencies places us in the digital realm and thus satisfies the need for the aforementioned requisite oversight, here too IRBs serve a vital role in fulfilling the third-party ethical assessment of data collection and data manipulation for the sake of research driven advertising or academic inquiry.<br />
<br />
Firms often explicitly state in the terms and conditions of using their sites that they reserve the right to sell or use an individual's personally volunteered information toward serving their own business needs. Although this activity is controlled and kept within defined limits by consumer privacy laws, vigilant monitoring is necessary by the Federal Trade Commission (FTC) and other such organizations to ensure that businesses are conducting themselves both fairly and lawfully. Because a user agreement is usually only displayed once (typically during the registration process), if it all, the gravity and seriousness ascribed to its contents is frequently undervalued by users, often relegated as unimportant and a low priority.<br />
<br />
As a majority of avid social media users are only concerned about a platform’s (superficially free) software and community assets -- expressed through its ability to cultivate social capital or the manner by which it advocates for a “more open and connected world” (e.g. Facebook) -- the embedded costs posed by data mining to users are subliminal and certainly not without their ramifications. Many users feel that the pursuit is an irresponsible practice, irrespective of the safeguards and stipulations found in user agreements, and consider it to be an activity in which social media firms should not engage.<ref>Facebook, [http://www.facebook.com/advertising/ Advertising]</ref><ref>http://mashable.com/2011/02/25/data-mining-social-marketing/</ref> Often for that reason, or purely on the rationale that the sum total of one’s social contributions could be aggregated and packaged up for sale at any time to an outside party, without due notification or option for opting out, many people who choose not to participate on Web-based social platforms due so for concerns that mirror those convictions. Increasing the transparency of what data is mined and whether a user is particularly vulnerable (i.e. for disregarding their privacy measures) has the potential to restore the necessary confidence in weary users for returning to the platform.<br />
<br />
===Use Case #2: Business===<br />
Much like the value that can be derived from mining social sites, a wide array of businesses has a similar opportunity in leveraging data mining techniques for improving their respective processes and ultimately, economic returns. Reducing expenses and maximizing profits is a shared challenge among all for-profit institutions. As a result, it is not uncommon for corporations spanning various disciplines to use mined data in conjunction with analytic and predictive methods for getting a more informed business perspective. Ethically, many of the issues that confront businesses with respect to data mining are similar to other contextual use cases: they revolve around the notion of privacy and the extent to which the resulting information can be shared with third-parties. Because the benefits of such activity can be used to “...reduce fraud, anticipate resource demand, increase acquisition and curb customer attrition,” the attractive nature of the process generally outweighs any lingering moral uncertainties.<ref>SAS Enterprise Analytics https://www.sas.com/en_us/software/analytics/enterprise-miner.html</ref><br />
<br />
===Use Case #3: Government===<br />
Privacy is a theme that pertains to almost every possible application of data mining as an information revealing mechanism. The case happens to be no different when it is discussed in the frame of government utilization. There are hundreds of available uses, with some of the most recently relevant being in the realm of predicting and and preventing terrorism.<br />
<br />
===Use Case #4: Healthcare===<br />
As more information related to individuals’ health records becomes digitized, data mining will become significantly more useful within the healthcare field. Patients’ health records, while used in aggregate, will provide significant insight into causes of specific illnesses. The success of data mining in this context hinges on the process of converting significant amounts of data into standardized digital formats on software frameworks with low storage and search costs.<br />
<br />
==See also==<br />
* [[Data Aggregation Online]]<br />
* [[Targeted Advertising (Online)]]<br />
* [[Geographic Information Systems]]<br />
*[[Recommender Systems]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Concepts]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Data_Mining&diff=56920Data Mining2016-04-21T16:27:14Z<p>Zzasuwa: /* User Profiling and Identification */</p>
<hr />
<div>[[File:DiagramDataMining.jpg|right|thumb|350px|This illustration shows the role [[Wikipedia:Data Mining|Data Mining]] plays in processing information for business use.]]<br />
'''Data Mining''' is the act of analyzing data from various perspectives and summarizing it into useful information, which combines aspects of artificial intelligence, machine learning, statistics and database systems. Software is implemented as one of many analytical tools used to analyze data. Through data mining, data is presented to users from many different angles, in various categories and relationships. On a more technical term, data mining is the process of realizing correlations or patterns among large fields of relative databases.<ref name="palace">[http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/index.htm Palace, Bill. "What Is Data Mining?" Data Mining. Anderson Graduate School of Management at UCLA, Mar. 1996. Web. 16 Dec. 2011.]</ref><br />
<br />
Data mining is used in many fields such as business, marketing, engineering, medicine, and the music and gaming industry. Businesses employ data mining techniques to gather information on their customers to better advertise and target future customers, while data mining is used to recognize patterns and designate categories for work in gaming, medicine, and music. Because personal information is gathered and studied, ethical concerns arise from the lack of anonymity and privacy of the subjects used in data mining techniques.<br />
<br />
<br />
==Process of Data Mining==<br />
The process of Data Mining requires the extraction of useful information from large sets of data stored in a uniform way, often in a database. The type of information extracted is relative to the type of data available, and the purpose of the data mining project itself – the information extracted could be modeled or represented of the entire set of data for example if the objective was to draw a conclusion of the dataset as a whole. <ref>http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf</ref><br />
<br />
Data mining consists of multiple sub-tasks:<ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Olaru, C., & Wehenkel, L. (1999). Data mining. IEEE Computer Applications in Power, 12(3), 19-25. doi:10.1109/67.773801]</ref><ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Shen Bin, Liu Yuan, & Wang Xiaoyi. (2010). Research on data mining models for the internet of things. 2010 International Conference on Image Analysis and Signal Processing (IASP) (pp. 127-132). Presented at the 2010 International Conference on Image Analysis and Signal Processing (IASP), IEEE. doi:10.1109/IASP.2010.5476146]</ref><br />
<br />
===Anomaly Detection=== <br />
Also called deviation detection, this is the “data cleaning” process where data miners attempt to identify unusual circumstances within the data that may belie anomalies or errors. The process includes program-aided searches of the data, parsing the data through filters that check for certain cases, and searching for unexplainable patterns within the data. The anomaly detection process often requires checking how the data was aggregated, and if human error had an effect on the data present.<br />
<br />
===Clustering=== <br />
[[File:Clustered.png|right|thumb|350px|A database of songs that has been visually represented as clusters of data which best represent the categories of songs present (ie. hip-hop, punk).]]<br />
Situations arise within datasets where there is a desire to know if similarities exist within the data, but no mechanism for detecting them exists. Clustering procedures within data mining aim to find similar objects within the data or to analyze correlations between attributes of the data. Clustering is also used to find topographical similarities within data sets.<br />
<br />
===Classification=== <br />
Use of mined data requires the input and output information to be formatted in a discrete way in order for the information to be useful. Classifying traits of both the input and output of the data minimizes ambiguity, and helps define unique qualities of the data, and the meaning of the data itself. This process also helps discover unique relationships that exist between the data present in the database (the input data), and information that is being presented as conclusive of the data set (the output data). Classification can occur in many formats, from labeling, to building models which fit the particular data set explicitly.<br />
<br />
===Regression=== <br />
The building of a transparent model which fits the data, and adequately conveys the information contained within the data set is the objective of the regression step of data mining. The use of statistical methods is the most common approach within the regression step, because it yields discrete output. Regression modeling also attempts to minimize error within the other steps of the data mining process.<br />
<br />
===Summarization===<br />
This task aims at producing representative descriptions of the data which provides context for the data. The summary process can take multiple formats, the most common of which is numerical analysis, which provides statistical quantification for patterns in the data like the mean or standard deviation found within the data set. These results are often represented in graphical formats – such as histograms or scatter plots. Qualitative data can be summarized by giving a list of trends or frequencies within a set of data. These results are useful because they represent the entirety of the data set in a much more digestible format.<br />
<br />
==Data Mining Tools==<br />
<br />
As data mining is becoming more popular to sift through the massive amounts of data available online, new tools have been cropping up to make the job simpler. Google, for example, has released a free tool called Correlate<ref>http://www.fastcompany.com/1755287/google-correlate-tool-gives-marketers-powerful-new-data-mining-tools</ref> aimed at helping corporations utilize the Google search database to draw conclusions on consumer behavior. MIT and Harvard have recently collaborated in the creation of MINE, a tool of unprecedented power which can analyze more data for more complex patterns than any tool previously available. It is said to approach the quality of human examination in terms of being able to pick up on nonstandard or deceptive patterns<ref>http://www.decodedscience.com/data-mining-tool-advances-mine-ranks-multiple-patterns/7959</ref>.<br />
<br />
==Applications of Data Mining==<br />
===Examples===<br />
A simple example of data mining is analyzing a large population, such as University of Michigan students, and determining simple characteristics that the data has, such as the proportion of the student body that is from each ethnic background.<br />
<br />
Before the term "data mining" came into popular use, many businesses had already implemented its technology. They used powerful computers to comb through quantitative data from supermarket scanners, and analyzed the resulting data for market research purposes. This process have been immensely increasing the precision of analysis, and at the same time decreasing the cost of research.<ref name="palace"/><br />
<br />
===Business===<br />
Data mining is commonly used in business <ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:bookitem&rft.title=Introduction+to+Data+Mining+and+its+Applications&rft.atitle=Data+Mining+in+Sales+Marketing+and+Finance&rft.creator=Sumathi%2C+S&rft.creator=Sivanandam%2C+S+N&rft.series=Studies+in+Computational+Intelligence&rft.date=2006-01-01&rft.pub=Springer+Berlin+Heidelberg&rft.isbn=9783540343509&rft.issn=1860-949X&rft.volume=29&rft.spage=411&rft.epage=438&rft_id=info:doi/10.1007%2F978-3-540-34351-6_18&rft.externalDBID=n%2Fa&rft.externalDocID=978-3-540-34351-6_133505_Chap18 Data Mining in Sales Marketing and Finance. (2006). Introduction to Data Mining and its Applications (Vol. 29, pp. 411-438). Berlin, Heidelberg: Springer Berlin Heidelberg.]</ref> to determine what demographics are buying what products, and to help firms predict customer preferences. Online retailers often track the ways which their clients interact with the content on their site. This leads to an extensive collection of data that can be used by data mining procedures to model representative "personas" of future clients who will visit the site. This has meaningful value for companies, because information generated from data mining practices can lead to more "click-throughs" and potentially, more "conversions" (sales).<br />
<br />
===Science/Academics===<br />
Data Mining also plays a crucial role in scientific analysis. The objective of many large research studies is to find patterns in experimental data. Many data mining procedures allow for applicable modeling of data in a way that is much faster and easier than doing so by hand.<br />
<br />
===Other Uses===<br />
Data Mining techniques have been used in a variety of other matters, such as in admissions processes in universities and political campaigns. <ref>http://communities.washingtontimes.com/neighborhood/political-potpourri/2012/nov/17/how-obama-won/</ref><br />
<ref>http://www.tnr.com/article/110862/data-mining-the-ineffective-future-affirmative-action-in-education#</ref> <br />
<br />
==Ethical Implications==<br />
Data mining is an inherently morally-neutral action, as it is simply the practice of working with large stored data sets and the process itself does not account for the way that the data was generated or will inevitably be used. The ethics of data mining come under scrutiny when the type of data being mined is something that the individuals or specific entities subject to the mining feel should not be disclosed for others to view. Issues also arise when sufficiently robust restrictions are not in place to hide the private characteristics of an individual’s personal identity in the online environment. Likewise, when firms neglect to take adequately tested precautionary measures for protecting a customer’s data as part of a larger collective set of consumer information, they are effectively opening all their clients’ data up to the possibility of unprotected digital consumption and exploitation.<br />
<br />
===User Profiling and Identification===<br />
In an attempt to build accurate models, data miners in online environments occasionally utilize the personal information of individuals who visit their sites. The information generally collected includes, but is not limited to, geographic identifiers, records of past behavior, or information that can identify an individual by a range of other intimate attributes like user account details or IP addresses. Many users do not actively realize that their information is being collected and many question the ethics of using an individual’s private information without their outright and formal consent.<ref>http://www.ecommercetimes.com/story/52616.html</ref> Additionally, data mining practices and tools have made the automated profiling of groups and individuals an easy task for a growing number of the technically literate. Using data mining to create customer profiling questions also raises ethical concerns regarding these practices due to the associated risks it creates for discrimination, de-individualization, and information asymmetries.<ref>Schermer, B.W. (2011). The limits of privacy in automated profiling and data mining. Computer Law and Security Report, 27(1), pp. 45-52.</ref><br />
<br />
===Distortion of Truth===<br />
Second to privacy concerns, distorting or misrepresenting trends in data that are not actually there together account for the other main ethical issue on the topic of data mining. The result of human or programmatic mishaps, generating flawed associations between data points can be severely costly to reputations, economic footings, and the continued confidence in the reliability of the data analysis method.<br />
<br />
===Anonymity and Ownership===<br />
Many information systems contain data on individuals, so maintaining anonymity of the data and informing those whose information is being used, exactly how it is used and to what effect, is not only important but in many cases a legally binding matter. Allowing individuals the freedom to opt in or opt out of data mining processes is one way to ensure some form of ethical responsibility.<br />
<br />
Discussion regarding the role of data ownership emerges as a natural consequence to its collection from disparate sources and its subsequent composite processing, analysis, and summarization. A contested point in some circumstances is the differentiation between raw source data (input) and the proprietary information product (output). Because the former, when in aggregate form, represents the constituent building blocks of the latter, the resulting interdependency spawns many debates surrounding who, if anyone, rightfully owns each intermediate information good and the finished analysis. Boiling down to where lines are drawn with respect to voluntary (or involuntary) data relinquishment and the perception of residual value, often isolated to only a single entity or two, the ground is ripe for dispute and ethical ambiguity.<br />
<br />
===Use Case #1: Social Networking===<br />
Mining data found on social networking sites poses a particular challenge, as a great deal of personal information can be found on individuals’ social media pages. It is not uncommon for social media firms like Facebook and Twitter to sell the information mined from users’ personal pages to advertisers and academic researchers. Despite the fact that this data is anonymized and/or collected from a large pool of public-facing accounts, there are still a number of valid sources for apprehension regarding its execution and overall legitimacy.<br />
<br />
To reduce users’ qualms surrounding this type of activity and to ensure that the ethical validity of all such experimentation meets a sound moral standard, committees in the form of [[Wikipedia:Institutional_review_board|institutional review boards]] (IRBs) are organized across both industry and the social science disciplines of higher-learning. They are entrusted with the responsibility for reviewing, monitoring, and approving of any biomedical or behavioral research involving humans.<ref>Wikipedia: Institutional review board https://en.wikipedia.org/wiki/Institutional_review_board</ref> Because the natural extension of examining humans’ behavioral tendencies places us in the digital realm and thus satisfies the need for the aforementioned requisite oversight, here too IRBs serve a vital role in fulfilling the third-party ethical assessment of data collection and data manipulation for the sake of research driven advertising or academic inquiry.<br />
<br />
Firms often explicitly state in the terms and conditions of using their sites that they reserve the right to sell or use an individual's personally volunteered information toward serving their own business needs. Although this activity is controlled and kept within defined limits by consumer privacy laws, vigilant monitoring is necessary by the Federal Trade Commission (FTC) and other such organizations to ensure that businesses are conducting themselves both fairly and lawfully. Because a user agreement is usually only displayed once (typically during the registration process), if it all, the gravity and seriousness ascribed to its contents is frequently undervalued by users, often relegated as unimportant and a low priority.<br />
<br />
As a majority of avid social media users are only concerned about a platform’s (superficially free) software and community assets -- expressed through its ability to cultivate social capital or the manner by which it advocates for a “more open and connected world” (e.g. Facebook) -- the embedded costs posed by data mining to users are subliminal and certainly not without their ramifications. Many users feel that the pursuit is an irresponsible practice, irrespective of the safeguards and stipulations found in user agreements, and consider it to be an activity in which social media firms should not engage.<ref>Facebook, [http://www.facebook.com/advertising/ Advertising]</ref><ref>http://mashable.com/2011/02/25/data-mining-social-marketing/</ref> Often for that reason, or purely on the rationale that the sum total of one’s social contributions could be aggregated and packaged up for sale at any time to an outside party, without due notification or option for opting out, many people who choose not to participate on Web-based social platforms due so for concerns that mirror those convictions. Increasing the transparency of what data is mined and whether a user is particularly vulnerable (i.e. for disregarding their privacy measures) has the potential to restore the necessary confidence in weary users for returning to the platform.<br />
<br />
===Use Case #2: Business===<br />
Much like the value that can be derived from mining social sites, a wide array of businesses has a similar opportunity in leveraging data mining techniques for improving their respective processes and ultimately, economic returns. Reducing expenses and maximizing profits is a shared challenge among all for-profit institutions. As a result, it is not uncommon for corporations spanning various disciplines to use mined data in conjunction with analytic and predictive methods for getting a more informed business perspective. Ethically, many of the issues that confront businesses with respect to data mining are similar to other contextual use cases: they revolve around the notion of privacy and the extent to which the resulting information can be shared with third-parties. Because the benefits of such activity can be used to “...reduce fraud, anticipate resource demand, increase acquisition and curb customer attrition,” the attractive nature of the process generally outweighs any lingering moral uncertainties.<ref>SAS Enterprise Analytics https://www.sas.com/en_us/software/analytics/enterprise-miner.html</ref><br />
<br />
===Use Case #3: Government===<br />
Privacy is a theme that pertains to almost every possible application of data mining as an information revealing mechanism. The case happens to be no different when it is discussed in the frame of government utilization. There are hundreds of available uses, with some of the most recently relevant being in the realm of predicting and and preventing terrorism.<br />
<br />
===Use Case #4: Healthcare===<br />
As more information related to individuals’ health records becomes digitized, data mining will become significantly more useful within the healthcare field. Patients’ health records, while used in aggregate, will provide significant insight into causes of specific illnesses. The success of data mining in this context hinges on the process of converting significant amounts of data into standardized digital formats on software frameworks with low storage and search costs.<br />
<br />
==See also==<br />
* [[Data Aggregation Online]]<br />
* [[Targeted Advertising (Online)]]<br />
* [[Geographic Information Systems]]<br />
*[[Recommender Systems]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Concepts]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Data_Mining&diff=56738Data Mining2016-04-16T04:26:18Z<p>Zzasuwa: /* Ethical Implications (Under Construction, 4/16/2016) */</p>
<hr />
<div>[[File:DiagramDataMining.jpg|right|thumb|350px|This illustration shows the role [[Wikipedia:Data Mining|Data Mining]] plays in processing information for business use.]]<br />
'''Data Mining''' is the act of analyzing data from various perspectives and summarizing it into useful information, which combines aspects of artificial intelligence, machine learning, statistics and database systems. Software is implemented as one of many analytical tools used to analyze data. Through data mining, data is presented to users from many different angles, in various categories and relationships. On a more technical term, data mining is the process of realizing correlations or patterns among large fields of relative databases.<ref name="palace">[http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/index.htm Palace, Bill. "What Is Data Mining?" Data Mining. Anderson Graduate School of Management at UCLA, Mar. 1996. Web. 16 Dec. 2011.]</ref><br />
<br />
Data mining is used in many fields such as business, marketing, engineering, medicine, and the music and gaming industry. Businesses employ data mining techniques to gather information on their customers to better advertise and target future customers, while data mining is used to recognize patterns and designate categories for work in gaming, medicine, and music. Because personal information is gathered and studied, ethical concerns arise from the lack of anonymity and privacy of the subjects used in data mining techniques.<br />
<br />
<br />
==Process of Data Mining==<br />
The process of Data Mining requires the extraction of useful information from large sets of data stored in a uniform way, often in a database. The type of information extracted is relative to the type of data available, and the purpose of the data mining project itself – the information extracted could be modeled or represented of the entire set of data for example if the objective was to draw a conclusion of the dataset as a whole. <ref>http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf</ref><br />
<br />
Data mining consists of multiple sub-tasks:<ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Olaru, C., & Wehenkel, L. (1999). Data mining. IEEE Computer Applications in Power, 12(3), 19-25. doi:10.1109/67.773801]</ref><ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Shen Bin, Liu Yuan, & Wang Xiaoyi. (2010). Research on data mining models for the internet of things. 2010 International Conference on Image Analysis and Signal Processing (IASP) (pp. 127-132). Presented at the 2010 International Conference on Image Analysis and Signal Processing (IASP), IEEE. doi:10.1109/IASP.2010.5476146]</ref><br />
<br />
===Anomaly Detection=== <br />
Also called deviation detection, this is the “data cleaning” process where data miners attempt to identify unusual circumstances within the data that may belie anomalies or errors. The process includes program-aided searches of the data, parsing the data through filters that check for certain cases, and searching for unexplainable patterns within the data. The anomaly detection process often requires checking how the data was aggregated, and if human error had an effect on the data present.<br />
<br />
===Clustering=== <br />
[[File:Clustered.png|right|thumb|350px|A database of songs that has been visually represented as clusters of data which best represent the categories of songs present (ie. hip-hop, punk).]]<br />
Situations arise within datasets where there is a desire to know if similarities exist within the data, but no mechanism for detecting them exists. Clustering procedures within data mining aim to find similar objects within the data or to analyze correlations between attributes of the data. Clustering is also used to find topographical similarities within data sets.<br />
<br />
===Classification=== <br />
Use of mined data requires the input and output information to be formatted in a discrete way in order for the information to be useful. Classifying traits of both the input and output of the data minimizes ambiguity, and helps define unique qualities of the data, and the meaning of the data itself. This process also helps discover unique relationships that exist between the data present in the database (the input data), and information that is being presented as conclusive of the data set (the output data). Classification can occur in many formats, from labeling, to building models which fit the particular data set explicitly.<br />
<br />
===Regression=== <br />
The building of a transparent model which fits the data, and adequately conveys the information contained within the data set is the objective of the regression step of data mining. The use of statistical methods is the most common approach within the regression step, because it yields discrete output. Regression modeling also attempts to minimize error within the other steps of the data mining process.<br />
<br />
===Summarization===<br />
This task aims at producing representative descriptions of the data which provides context for the data. The summary process can take multiple formats, the most common of which is numerical analysis, which provides statistical quantification for patterns in the data like the mean or standard deviation found within the data set. These results are often represented in graphical formats – such as histograms or scatter plots. Qualitative data can be summarized by giving a list of trends or frequencies within a set of data. These results are useful because they represent the entirety of the data set in a much more digestible format.<br />
<br />
==Data Mining Tools==<br />
<br />
As data mining is becoming more popular to sift through the massive amounts of data available online, new tools have been cropping up to make the job simpler. Google, for example, has released a free tool called Correlate<ref>http://www.fastcompany.com/1755287/google-correlate-tool-gives-marketers-powerful-new-data-mining-tools</ref> aimed at helping corporations utilize the Google search database to draw conclusions on consumer behavior. MIT and Harvard have recently collaborated in the creation of MINE, a tool of unprecedented power which can analyze more data for more complex patterns than any tool previously available. It is said to approach the quality of human examination in terms of being able to pick up on nonstandard or deceptive patterns<ref>http://www.decodedscience.com/data-mining-tool-advances-mine-ranks-multiple-patterns/7959</ref>.<br />
<br />
==Applications of Data Mining==<br />
===Examples===<br />
A simple example of data mining is analyzing a large population, such as University of Michigan students, and determining simple characteristics that the data has, such as the proportion of the student body that is from each ethnic background.<br />
<br />
Before the term "data mining" came into popular use, many businesses had already implemented its technology. They used powerful computers to comb through quantitative data from supermarket scanners, and analyzed the resulting data for market research purposes. This process have been immensely increasing the precision of analysis, and at the same time decreasing the cost of research.<ref name="palace"/><br />
<br />
===Business===<br />
Data mining is commonly used in business <ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:bookitem&rft.title=Introduction+to+Data+Mining+and+its+Applications&rft.atitle=Data+Mining+in+Sales+Marketing+and+Finance&rft.creator=Sumathi%2C+S&rft.creator=Sivanandam%2C+S+N&rft.series=Studies+in+Computational+Intelligence&rft.date=2006-01-01&rft.pub=Springer+Berlin+Heidelberg&rft.isbn=9783540343509&rft.issn=1860-949X&rft.volume=29&rft.spage=411&rft.epage=438&rft_id=info:doi/10.1007%2F978-3-540-34351-6_18&rft.externalDBID=n%2Fa&rft.externalDocID=978-3-540-34351-6_133505_Chap18 Data Mining in Sales Marketing and Finance. (2006). Introduction to Data Mining and its Applications (Vol. 29, pp. 411-438). Berlin, Heidelberg: Springer Berlin Heidelberg.]</ref> to determine what demographics are buying what products, and to help firms predict customer preferences. Online retailers often track the ways which their clients interact with the content on their site. This leads to an extensive collection of data that can be used by data mining procedures to model representative "personas" of future clients who will visit the site. This has meaningful value for companies, because information generated from data mining practices can lead to more "click-throughs" and potentially, more "conversions" (sales).<br />
<br />
===Science/Academics===<br />
Data Mining also plays a crucial role in scientific analysis. The objective of many large research studies is to find patterns in experimental data. Many data mining procedures allow for applicable modeling of data in a way that is much faster and easier than doing so by hand.<br />
<br />
===Other Uses===<br />
Data Mining techniques have been used in a variety of other matters, such as in admissions processes in universities and political campaigns. <ref>http://communities.washingtontimes.com/neighborhood/political-potpourri/2012/nov/17/how-obama-won/</ref><br />
<ref>http://www.tnr.com/article/110862/data-mining-the-ineffective-future-affirmative-action-in-education#</ref> <br />
<br />
==Ethical Implications==<br />
Data mining is an inherently morally-neutral action, as it is simply the practice of working with large stored data sets and the process itself does not account for the way that the data was generated or will inevitably be used. The ethics of data mining come under scrutiny when the type of data being mined is something that the individuals or specific entities subject to the mining feel should not be disclosed for others to view. Issues also arise when sufficiently robust restrictions are not in place to hide the private characteristics of an individual’s personal identity in the online environment. Likewise, when firms neglect to take adequately tested precautionary measures for protecting a customer’s data as part of a larger collective set of consumer information, they are effectively opening all their clients’ data up to the possibility of unprotected digital consumption and exploitation.<br />
<br />
===User Profiling and Identification===<br />
In an attempt to build accurate models, data miners in online environments occasionally utilize the personal information of individuals who visit their sites. The information generally collected includes, but is not limited to, geographic identifiers, records of past behavior, or information that can identify an individual by a range of other intimate attributes like user account details or IP address. Many users do not actively realize that their information is being collected and many question the ethics of using an individual’s private information without their outright and formal consent.<ref>http://www.ecommercetimes.com/story/52616.html</ref> Additionally, data mining practices and tools have made the automated profiling of groups and individuals an easy task for a growing number of the technically literate. Using data mining to create customer profiling questions also raises ethical concerns regarding these practices due to the associated risks it creates for discrimination, de-individualization, and information asymmetries.<ref>Schermer, B.W. (2011). The limits of privacy in automated profiling and data mining. Computer Law and Security Report, 27(1), pp. 45-52.</ref><br />
<br />
===Distortion of Truth===<br />
Second to privacy concerns, distorting or misrepresenting trends in data that are not actually there together account for the other main ethical issue on the topic of data mining. The result of human or programmatic mishaps, generating flawed associations between data points can be severely costly to reputations, economic footings, and the continued confidence in the reliability of the data analysis method.<br />
<br />
===Anonymity and Ownership===<br />
Many information systems contain data on individuals, so maintaining anonymity of the data and informing those whose information is being used, exactly how it is used and to what effect, is not only important but in many cases a legally binding matter. Allowing individuals the freedom to opt in or opt out of data mining processes is one way to ensure some form of ethical responsibility.<br />
<br />
Discussion regarding the role of data ownership emerges as a natural consequence to its collection from disparate sources and its subsequent composite processing, analysis, and summarization. A contested point in some circumstances is the differentiation between raw source data (input) and the proprietary information product (output). Because the former, when in aggregate form, represents the constituent building blocks of the latter, the resulting interdependency spawns many debates surrounding who, if anyone, rightfully owns each intermediate information good and the finished analysis. Boiling down to where lines are drawn with respect to voluntary (or involuntary) data relinquishment and the perception of residual value, often isolated to only a single entity or two, the ground is ripe for dispute and ethical ambiguity.<br />
<br />
===Use Case #1: Social Networking===<br />
Mining data found on social networking sites poses a particular challenge, as a great deal of personal information can be found on individuals’ social media pages. It is not uncommon for social media firms like Facebook and Twitter to sell the information mined from users’ personal pages to advertisers and academic researchers. Despite the fact that this data is anonymized and/or collected from a large pool of public-facing accounts, there are still a number of valid sources for apprehension regarding its execution and overall legitimacy.<br />
<br />
To reduce users’ qualms surrounding this type of activity and to ensure that the ethical validity of all such experimentation meets a sound moral standard, committees in the form of [[Wikipedia:Institutional_review_board|institutional review boards]] (IRBs) are organized across both industry and the social science disciplines of higher-learning. They are entrusted with the responsibility for reviewing, monitoring, and approving of any biomedical or behavioral research involving humans.<ref>Wikipedia: Institutional review board https://en.wikipedia.org/wiki/Institutional_review_board</ref> Because the natural extension of examining humans’ behavioral tendencies places us in the digital realm and thus satisfies the need for the aforementioned requisite oversight, here too IRBs serve a vital role in fulfilling the third-party ethical assessment of data collection and data manipulation for the sake of research driven advertising or academic inquiry.<br />
<br />
Firms often explicitly state in the terms and conditions of using their sites that they reserve the right to sell or use an individual's personally volunteered information toward serving their own business needs. Although this activity is controlled and kept within defined limits by consumer privacy laws, vigilant monitoring is necessary by the Federal Trade Commission (FTC) and other such organizations to ensure that businesses are conducting themselves both fairly and lawfully. Because a user agreement is usually only displayed once (typically during the registration process), if it all, the gravity and seriousness ascribed to its contents is frequently undervalued by users, often relegated as unimportant and a low priority.<br />
<br />
As a majority of avid social media users are only concerned about a platform’s (superficially free) software and community assets -- expressed through its ability to cultivate social capital or the manner by which it advocates for a “more open and connected world” (e.g. Facebook) -- the embedded costs posed by data mining to users are subliminal and certainly not without their ramifications. Many users feel that the pursuit is an irresponsible practice, irrespective of the safeguards and stipulations found in user agreements, and consider it to be an activity in which social media firms should not engage.<ref>Facebook, [http://www.facebook.com/advertising/ Advertising]</ref><ref>http://mashable.com/2011/02/25/data-mining-social-marketing/</ref> Often for that reason, or purely on the rationale that the sum total of one’s social contributions could be aggregated and packaged up for sale at any time to an outside party, without due notification or option for opting out, many people who choose not to participate on Web-based social platforms due so for concerns that mirror those convictions. Increasing the transparency of what data is mined and whether a user is particularly vulnerable (i.e. for disregarding their privacy measures) has the potential to restore the necessary confidence in weary users for returning to the platform.<br />
<br />
===Use Case #2: Business===<br />
Much like the value that can be derived from mining social sites, a wide array of businesses has a similar opportunity in leveraging data mining techniques for improving their respective processes and ultimately, economic returns. Reducing expenses and maximizing profits is a shared challenge among all for-profit institutions. As a result, it is not uncommon for corporations spanning various disciplines to use mined data in conjunction with analytic and predictive methods for getting a more informed business perspective. Ethically, many of the issues that confront businesses with respect to data mining are similar to other contextual use cases: they revolve around the notion of privacy and the extent to which the resulting information can be shared with third-parties. Because the benefits of such activity can be used to “...reduce fraud, anticipate resource demand, increase acquisition and curb customer attrition,” the attractive nature of the process generally outweighs any lingering moral uncertainties.<ref>SAS Enterprise Analytics https://www.sas.com/en_us/software/analytics/enterprise-miner.html</ref><br />
<br />
===Use Case #3: Government===<br />
Privacy is a theme that pertains to almost every possible application of data mining as an information revealing mechanism. The case happens to be no different when it is discussed in the frame of government utilization. There are hundreds of available uses, with some of the most recently relevant being in the realm of predicting and and preventing terrorism.<br />
<br />
===Use Case #4: Healthcare===<br />
As more information related to individuals’ health records becomes digitized, data mining will become significantly more useful within the healthcare field. Patients’ health records, while used in aggregate, will provide significant insight into causes of specific illnesses. The success of data mining in this context hinges on the process of converting significant amounts of data into standardized digital formats on software frameworks with low storage and search costs.<br />
<br />
==See also==<br />
* [[Data Aggregation Online]]<br />
* [[Targeted Advertising (Online)]]<br />
* [[Geographic Information Systems]]<br />
*[[Recommender Systems]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Concepts]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=Data_Mining&diff=56737Data Mining2016-04-16T04:25:56Z<p>Zzasuwa: /* Ethical Implications (Under Construction, 4/3/2016) */</p>
<hr />
<div>[[File:DiagramDataMining.jpg|right|thumb|350px|This illustration shows the role [[Wikipedia:Data Mining|Data Mining]] plays in processing information for business use.]]<br />
'''Data Mining''' is the act of analyzing data from various perspectives and summarizing it into useful information, which combines aspects of artificial intelligence, machine learning, statistics and database systems. Software is implemented as one of many analytical tools used to analyze data. Through data mining, data is presented to users from many different angles, in various categories and relationships. On a more technical term, data mining is the process of realizing correlations or patterns among large fields of relative databases.<ref name="palace">[http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/index.htm Palace, Bill. "What Is Data Mining?" Data Mining. Anderson Graduate School of Management at UCLA, Mar. 1996. Web. 16 Dec. 2011.]</ref><br />
<br />
Data mining is used in many fields such as business, marketing, engineering, medicine, and the music and gaming industry. Businesses employ data mining techniques to gather information on their customers to better advertise and target future customers, while data mining is used to recognize patterns and designate categories for work in gaming, medicine, and music. Because personal information is gathered and studied, ethical concerns arise from the lack of anonymity and privacy of the subjects used in data mining techniques.<br />
<br />
<br />
==Process of Data Mining==<br />
The process of Data Mining requires the extraction of useful information from large sets of data stored in a uniform way, often in a database. The type of information extracted is relative to the type of data available, and the purpose of the data mining project itself – the information extracted could be modeled or represented of the entire set of data for example if the objective was to draw a conclusion of the dataset as a whole. <ref>http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf</ref><br />
<br />
Data mining consists of multiple sub-tasks:<ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Olaru, C., & Wehenkel, L. (1999). Data mining. IEEE Computer Applications in Power, 12(3), 19-25. doi:10.1109/67.773801]</ref><ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data+mining&rft.jtitle=IEEE+Computer+Applications+in+Power&rft.au=Wehenkel%2C+L&rft.au=Olaru%2C+C&rft.date=1999-03-01&rft.pub=IEEE-INST+ELECTRICAL+ELECTRONICS+ENGINEERS+INC&rft.issn=0895-0156&rft.volume=12&rft.issue=3&rft.spage=19&rft.epage=25&rft_id=info:doi/10.1109%2F67.773801&rft.externalDBID=ECAP&rft.externalDocID=67 Shen Bin, Liu Yuan, & Wang Xiaoyi. (2010). Research on data mining models for the internet of things. 2010 International Conference on Image Analysis and Signal Processing (IASP) (pp. 127-132). Presented at the 2010 International Conference on Image Analysis and Signal Processing (IASP), IEEE. doi:10.1109/IASP.2010.5476146]</ref><br />
<br />
===Anomaly Detection=== <br />
Also called deviation detection, this is the “data cleaning” process where data miners attempt to identify unusual circumstances within the data that may belie anomalies or errors. The process includes program-aided searches of the data, parsing the data through filters that check for certain cases, and searching for unexplainable patterns within the data. The anomaly detection process often requires checking how the data was aggregated, and if human error had an effect on the data present.<br />
<br />
===Clustering=== <br />
[[File:Clustered.png|right|thumb|350px|A database of songs that has been visually represented as clusters of data which best represent the categories of songs present (ie. hip-hop, punk).]]<br />
Situations arise within datasets where there is a desire to know if similarities exist within the data, but no mechanism for detecting them exists. Clustering procedures within data mining aim to find similar objects within the data or to analyze correlations between attributes of the data. Clustering is also used to find topographical similarities within data sets.<br />
<br />
===Classification=== <br />
Use of mined data requires the input and output information to be formatted in a discrete way in order for the information to be useful. Classifying traits of both the input and output of the data minimizes ambiguity, and helps define unique qualities of the data, and the meaning of the data itself. This process also helps discover unique relationships that exist between the data present in the database (the input data), and information that is being presented as conclusive of the data set (the output data). Classification can occur in many formats, from labeling, to building models which fit the particular data set explicitly.<br />
<br />
===Regression=== <br />
The building of a transparent model which fits the data, and adequately conveys the information contained within the data set is the objective of the regression step of data mining. The use of statistical methods is the most common approach within the regression step, because it yields discrete output. Regression modeling also attempts to minimize error within the other steps of the data mining process.<br />
<br />
===Summarization===<br />
This task aims at producing representative descriptions of the data which provides context for the data. The summary process can take multiple formats, the most common of which is numerical analysis, which provides statistical quantification for patterns in the data like the mean or standard deviation found within the data set. These results are often represented in graphical formats – such as histograms or scatter plots. Qualitative data can be summarized by giving a list of trends or frequencies within a set of data. These results are useful because they represent the entirety of the data set in a much more digestible format.<br />
<br />
==Data Mining Tools==<br />
<br />
As data mining is becoming more popular to sift through the massive amounts of data available online, new tools have been cropping up to make the job simpler. Google, for example, has released a free tool called Correlate<ref>http://www.fastcompany.com/1755287/google-correlate-tool-gives-marketers-powerful-new-data-mining-tools</ref> aimed at helping corporations utilize the Google search database to draw conclusions on consumer behavior. MIT and Harvard have recently collaborated in the creation of MINE, a tool of unprecedented power which can analyze more data for more complex patterns than any tool previously available. It is said to approach the quality of human examination in terms of being able to pick up on nonstandard or deceptive patterns<ref>http://www.decodedscience.com/data-mining-tool-advances-mine-ranks-multiple-patterns/7959</ref>.<br />
<br />
==Applications of Data Mining==<br />
===Examples===<br />
A simple example of data mining is analyzing a large population, such as University of Michigan students, and determining simple characteristics that the data has, such as the proportion of the student body that is from each ethnic background.<br />
<br />
Before the term "data mining" came into popular use, many businesses had already implemented its technology. They used powerful computers to comb through quantitative data from supermarket scanners, and analyzed the resulting data for market research purposes. This process have been immensely increasing the precision of analysis, and at the same time decreasing the cost of research.<ref name="palace"/><br />
<br />
===Business===<br />
Data mining is commonly used in business <ref>[http://dl2af5jf3e.search.serialssolutions.com.proxy.lib.umich.edu/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:bookitem&rft.title=Introduction+to+Data+Mining+and+its+Applications&rft.atitle=Data+Mining+in+Sales+Marketing+and+Finance&rft.creator=Sumathi%2C+S&rft.creator=Sivanandam%2C+S+N&rft.series=Studies+in+Computational+Intelligence&rft.date=2006-01-01&rft.pub=Springer+Berlin+Heidelberg&rft.isbn=9783540343509&rft.issn=1860-949X&rft.volume=29&rft.spage=411&rft.epage=438&rft_id=info:doi/10.1007%2F978-3-540-34351-6_18&rft.externalDBID=n%2Fa&rft.externalDocID=978-3-540-34351-6_133505_Chap18 Data Mining in Sales Marketing and Finance. (2006). Introduction to Data Mining and its Applications (Vol. 29, pp. 411-438). Berlin, Heidelberg: Springer Berlin Heidelberg.]</ref> to determine what demographics are buying what products, and to help firms predict customer preferences. Online retailers often track the ways which their clients interact with the content on their site. This leads to an extensive collection of data that can be used by data mining procedures to model representative "personas" of future clients who will visit the site. This has meaningful value for companies, because information generated from data mining practices can lead to more "click-throughs" and potentially, more "conversions" (sales).<br />
<br />
===Science/Academics===<br />
Data Mining also plays a crucial role in scientific analysis. The objective of many large research studies is to find patterns in experimental data. Many data mining procedures allow for applicable modeling of data in a way that is much faster and easier than doing so by hand.<br />
<br />
===Other Uses===<br />
Data Mining techniques have been used in a variety of other matters, such as in admissions processes in universities and political campaigns. <ref>http://communities.washingtontimes.com/neighborhood/political-potpourri/2012/nov/17/how-obama-won/</ref><br />
<ref>http://www.tnr.com/article/110862/data-mining-the-ineffective-future-affirmative-action-in-education#</ref> <br />
<br />
==Ethical Implications (Under Construction, 4/16/2016)==<br />
Data mining is an inherently morally-neutral action, as it is simply the practice of working with large stored data sets and the process itself does not account for the way that the data was generated or will inevitably be used. The ethics of data mining come under scrutiny when the type of data being mined is something that the individuals or specific entities subject to the mining feel should not be disclosed for others to view. Issues also arise when sufficiently robust restrictions are not in place to hide the private characteristics of an individual’s personal identity in the online environment. Likewise, when firms neglect to take adequately tested precautionary measures for protecting a customer’s data as part of a larger collective set of consumer information, they are effectively opening all their clients’ data up to the possibility of unprotected digital consumption and exploitation.<br />
<br />
===User Profiling and Identification===<br />
In an attempt to build accurate models, data miners in online environments occasionally utilize the personal information of individuals who visit their sites. The information generally collected includes, but is not limited to, geographic identifiers, records of past behavior, or information that can identify an individual by a range of other intimate attributes like user account details or IP address. Many users do not actively realize that their information is being collected and many question the ethics of using an individual’s private information without their outright and formal consent.<ref>http://www.ecommercetimes.com/story/52616.html</ref> Additionally, data mining practices and tools have made the automated profiling of groups and individuals an easy task for a growing number of the technically literate. Using data mining to create customer profiling questions also raises ethical concerns regarding these practices due to the associated risks it creates for discrimination, de-individualization, and information asymmetries.<ref>Schermer, B.W. (2011). The limits of privacy in automated profiling and data mining. Computer Law and Security Report, 27(1), pp. 45-52.</ref><br />
<br />
===Distortion of Truth===<br />
Second to privacy concerns, distorting or misrepresenting trends in data that are not actually there together account for the other main ethical issue on the topic of data mining. The result of human or programmatic mishaps, generating flawed associations between data points can be severely costly to reputations, economic footings, and the continued confidence in the reliability of the data analysis method.<br />
<br />
===Anonymity and Ownership===<br />
Many information systems contain data on individuals, so maintaining anonymity of the data and informing those whose information is being used, exactly how it is used and to what effect, is not only important but in many cases a legally binding matter. Allowing individuals the freedom to opt in or opt out of data mining processes is one way to ensure some form of ethical responsibility.<br />
<br />
Discussion regarding the role of data ownership emerges as a natural consequence to its collection from disparate sources and its subsequent composite processing, analysis, and summarization. A contested point in some circumstances is the differentiation between raw source data (input) and the proprietary information product (output). Because the former, when in aggregate form, represents the constituent building blocks of the latter, the resulting interdependency spawns many debates surrounding who, if anyone, rightfully owns each intermediate information good and the finished analysis. Boiling down to where lines are drawn with respect to voluntary (or involuntary) data relinquishment and the perception of residual value, often isolated to only a single entity or two, the ground is ripe for dispute and ethical ambiguity.<br />
<br />
===Use Case #1: Social Networking===<br />
Mining data found on social networking sites poses a particular challenge, as a great deal of personal information can be found on individuals’ social media pages. It is not uncommon for social media firms like Facebook and Twitter to sell the information mined from users’ personal pages to advertisers and academic researchers. Despite the fact that this data is anonymized and/or collected from a large pool of public-facing accounts, there are still a number of valid sources for apprehension regarding its execution and overall legitimacy.<br />
<br />
To reduce users’ qualms surrounding this type of activity and to ensure that the ethical validity of all such experimentation meets a sound moral standard, committees in the form of [[Wikipedia:Institutional_review_board|institutional review boards]] (IRBs) are organized across both industry and the social science disciplines of higher-learning. They are entrusted with the responsibility for reviewing, monitoring, and approving of any biomedical or behavioral research involving humans.<ref>Wikipedia: Institutional review board https://en.wikipedia.org/wiki/Institutional_review_board</ref> Because the natural extension of examining humans’ behavioral tendencies places us in the digital realm and thus satisfies the need for the aforementioned requisite oversight, here too IRBs serve a vital role in fulfilling the third-party ethical assessment of data collection and data manipulation for the sake of research driven advertising or academic inquiry.<br />
<br />
Firms often explicitly state in the terms and conditions of using their sites that they reserve the right to sell or use an individual's personally volunteered information toward serving their own business needs. Although this activity is controlled and kept within defined limits by consumer privacy laws, vigilant monitoring is necessary by the Federal Trade Commission (FTC) and other such organizations to ensure that businesses are conducting themselves both fairly and lawfully. Because a user agreement is usually only displayed once (typically during the registration process), if it all, the gravity and seriousness ascribed to its contents is frequently undervalued by users, often relegated as unimportant and a low priority.<br />
<br />
As a majority of avid social media users are only concerned about a platform’s (superficially free) software and community assets -- expressed through its ability to cultivate social capital or the manner by which it advocates for a “more open and connected world” (e.g. Facebook) -- the embedded costs posed by data mining to users are subliminal and certainly not without their ramifications. Many users feel that the pursuit is an irresponsible practice, irrespective of the safeguards and stipulations found in user agreements, and consider it to be an activity in which social media firms should not engage.<ref>Facebook, [http://www.facebook.com/advertising/ Advertising]</ref><ref>http://mashable.com/2011/02/25/data-mining-social-marketing/</ref> Often for that reason, or purely on the rationale that the sum total of one’s social contributions could be aggregated and packaged up for sale at any time to an outside party, without due notification or option for opting out, many people who choose not to participate on Web-based social platforms due so for concerns that mirror those convictions. Increasing the transparency of what data is mined and whether a user is particularly vulnerable (i.e. for disregarding their privacy measures) has the potential to restore the necessary confidence in weary users for returning to the platform.<br />
<br />
===Use Case #2: Business===<br />
Much like the value that can be derived from mining social sites, a wide array of businesses has a similar opportunity in leveraging data mining techniques for improving their respective processes and ultimately, economic returns. Reducing expenses and maximizing profits is a shared challenge among all for-profit institutions. As a result, it is not uncommon for corporations spanning various disciplines to use mined data in conjunction with analytic and predictive methods for getting a more informed business perspective. Ethically, many of the issues that confront businesses with respect to data mining are similar to other contextual use cases: they revolve around the notion of privacy and the extent to which the resulting information can be shared with third-parties. Because the benefits of such activity can be used to “...reduce fraud, anticipate resource demand, increase acquisition and curb customer attrition,” the attractive nature of the process generally outweighs any lingering moral uncertainties.<ref>SAS Enterprise Analytics https://www.sas.com/en_us/software/analytics/enterprise-miner.html</ref><br />
<br />
===Use Case #3: Government===<br />
Privacy is a theme that pertains to almost every possible application of data mining as an information revealing mechanism. The case happens to be no different when it is discussed in the frame of government utilization. There are hundreds of available uses, with some of the most recently relevant being in the realm of predicting and and preventing terrorism.<br />
<br />
===Use Case #4: Healthcare===<br />
As more information related to individuals’ health records becomes digitized, data mining will become significantly more useful within the healthcare field. Patients’ health records, while used in aggregate, will provide significant insight into causes of specific illnesses. The success of data mining in this context hinges on the process of converting significant amounts of data into standardized digital formats on software frameworks with low storage and search costs.<br />
<br />
==See also==<br />
* [[Data Aggregation Online]]<br />
* [[Targeted Advertising (Online)]]<br />
* [[Geographic Information Systems]]<br />
*[[Recommender Systems]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[category: Concepts]]<br />
[[category: Information Ethics]]<br />
[[category: Virtual Environments, Concerns, & Issues]]</div>Zzasuwahttp://si410wiki.sites.uofmhosting.net/index.php?title=File:Facebook_graph.jpeg&diff=56546File:Facebook graph.jpeg2016-04-12T00:54:59Z<p>Zzasuwa: </p>
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