Difference between revisions of "Reality Mining"
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===Definition=== | ===Definition=== | ||
| + | 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> | ||
| − | + | 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 | + | |
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===Relations to Business Intelligence=== | ===Relations to Business Intelligence=== | ||
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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. | 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. | ||
| − | 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 | + | 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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| + | MIT Media Lab Reality Mining | ||
===Machine Perception and Learning of Complex Social Systems=== | ===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. | 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. | ||
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. | 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. | ||
| − | 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. | + | 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: | ||
*How do social networks evolve over time? | *How do social networks evolve over time? | ||
*How entropic (predictable) are most people's lives? | *How entropic (predictable) are most people's lives? | ||
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*Can the topology of a social network be inferred from only proximity data? | *Can the topology of a social network be inferred from only proximity data? | ||
*How can we change a group's interactions to promote better functioning? | *How can we change a group's interactions to promote better functioning? | ||
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[[Category:Book Pages]] | [[Category:Book Pages]] | ||
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Revision as of 02:13, 13 June 2011
Definition
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.[1]
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[2].
Relations to Business Intelligence
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.
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 [3], [4]
MIT Media Lab Reality Mining
Machine Perception and Learning of Complex Social Systems
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.
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. 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.[5]
Reality Mining can be used to answer many questions, including the following:
- How do social networks evolve over time?
- How entropic (predictable) are most people's lives?
- How does information flow?
- Can the topology of a social network be inferred from only proximity data?
- How can we change a group's interactions to promote better functioning?
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