Difference between revisions of "Reality Mining"

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===Definition===
 
===Definition===
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Reality Mining is a term used to describe the collection and analysis of machine-sensed environmental data pertaining to human social behavior, with the goal of identifying predictable patterns of behavior.<ref>http://radar.oreilly.com/2008/05/the-results-of-reality-mining.html O'Reilly</ref>
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Reality mining is term used to describe the collection of information generated by everyday objects and the subsequent processing of it. This information can give a series of important clues and keys to the way life is lived. For instance, gathering information over time can help one to understand problems or trends in that information.
  
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Reality Mining studies human interactions based on the usage of wireless devices such as mobile phones and GPS systems providing a more accurate picture of what people do, where they go, and with whom they communicate with rather than from more subjective sources such as a people's own account. Reality mining is one aspect of [[Digital Footprint|digital footprint]] analysis<ref>http://www.businessweek.com/technology/content/mar2008/tc20080323_387127.htm BusinessWeek, 2008</ref>.  
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Reality Mining studies human interactions based on the usage of wireless devices such as mobile phones and GPS systems providing a more accurate picture of what people do, where they go, and with whom they communicate with rather than from more subjective sources such as a people's own account. Reality mining is one aspect of [[Digital Footprint|digital footprint]] analysis<ref>BusinessWeek, 2008. http://www.businessweek.com/technology/content/mar2008/tc20080323_387127.htm </ref>. Reality mining was used to look at Google search terms around cold and flu symptoms in order to predict flu epidemics. <ref>Google.com Flu Trends Map http://www.google.org/flutrends/</ref>
  
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===Relations to Business Intelligence===
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Civic developer Max Ogden's created a collaborative reality mining experiment called PortlandSmells. It encouraged citizens of Portland, Oregon to report what they smelled in Portland and provided a way for them to upload this information by mobile phone. What Ogden found was that the application was also very suited for citizens in reporting dangerous smells and indicative of spills in the city. The city later commissioned Ogden to make a toxic hazard reporting map in order to serve this purpose better.
  
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Informed business intelligence using "reality mining" supports better business decision-making based on employee behaviors. Gathering intelligence to help improve the business efficiency by monitoring, analyzing and fine tuning the employee's footprint based on what is efficient and what will yield more productive employees. It is important to observe people in the workplace and understand how they interact with their equipment, devices and applications. This observation allows us the means to obtain an “electronic footprint” or eFootPrint of these activities. We focus on what the employee does in their job, where they spend their time, how efficient are they performing it and how it can be improved.
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==References==
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<references />
  
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Management insights based on Honest Signals were named a `Breakthrough Idea of 2009' by Harvard Business Review, and Reality Mining was declared `a technology poised to change the world' by Technology Review.  This new view of human nature, organizations, and society is the subject of Sandy Pentland's book <ref>http://www.amazon.com/gp/product/0262162563?linkCode=shr&camp=213733&creative=393185&tag=haztectal-20 Honest Signals],' published by MIT Press</ref>, <ref>http://en.wikipedia.org/wiki/Reality_mining</ref>
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[[Category:Book Pages]]
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[[Category:Finished]]
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[[Category:Illustrated V2]]
  
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MIT Media Lab Reality Mining
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===Machine Perception and Learning of Complex Social Systems===
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Reality Mining defines the collection of machine-sensed environmental data pertaining to human social behavior. This new paradigm of data mining makes possible the modeling of conversation context, proximity sensing, and temporospatial location throughout large communities of individuals. Mobile phones (and similarly innocuous devices) are used for data collection, opening social network analysis to new methods of empirical stochastic modeling.
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The original Reality Mining experiment is one of the largest mobile phone projects attempted in academia. Our research agenda takes advantage of the increasingly widespread use of mobile phones to provide insight into the dynamics of both individual and group behavior. By leveraging recent advances in machine learning we are building generative models that can be used to predict what a single user will do next, as well as model behavior of large organizations.
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We have captured communication, proximity, location, and activity information from 100 subjects at MIT over the course of the 2004-2005 academic year. This data represents over 350,000 hours (~40 years) of continuous data on human behavior.<ref>MIT Media Lab Reality Mining http://reality.media.mit.edu/</ref>
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Reality Mining can be used to answer many questions, including the following:
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*How do social networks evolve over time?
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*How entropic (predictable) are most people's lives?
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*How does information flow?
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*Can the topology of a social network be inferred from only proximity data?
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*How can we change a group's interactions to promote better functioning?
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[[Category:Book Pages]]
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[[Category:Marked for Editing]]
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Latest revision as of 23:39, 7 August 2012

Definition

Reality mining is term used to describe the collection of information generated by everyday objects and the subsequent processing of it. This information can give a series of important clues and keys to the way life is lived. For instance, gathering information over time can help one to understand problems or trends in that information.

Reality Mining studies human interactions based on the usage of wireless devices such as mobile phones and GPS systems providing a more accurate picture of what people do, where they go, and with whom they communicate with rather than from more subjective sources such as a people's own account. Reality mining is one aspect of digital footprint analysis[1]. Reality mining was used to look at Google search terms around cold and flu symptoms in order to predict flu epidemics. [2]

Civic developer Max Ogden's created a collaborative reality mining experiment called PortlandSmells. It encouraged citizens of Portland, Oregon to report what they smelled in Portland and provided a way for them to upload this information by mobile phone. What Ogden found was that the application was also very suited for citizens in reporting dangerous smells and indicative of spills in the city. The city later commissioned Ogden to make a toxic hazard reporting map in order to serve this purpose better.

References

  1. ↑ BusinessWeek, 2008. http://www.businessweek.com/technology/content/mar2008/tc20080323_387127.htm
  2. ↑ Google.com Flu Trends Map http://www.google.org/flutrends/
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