Reality mining is the collection and analysis of machine-sensed environmental data pertaining to human social behavior, with the goal of identifying predictable patterns of behavior.
It was declared to be one of the “10 technologies most likely to change the way we live” by Technology Review Magazine.
This new paradigm of data mining makes possible the modelling 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 modelling.
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.
communication, proximity, location, and activity information
The research questions we are addressing include:
- 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?
“Reality Mining” — the gathering of data based on the activities of people in a given environment — was a major trend to emerge out of, and contributor to, Big Data.
“If you look at an office environment there is an extraordinary amount of data to look at. For example, what gestures people are making, where are they looking, what conversations are they having, how much are they smiling when they speak to each other?” he said.
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Researchers say they can get a more accurate picture of what people do, where they go, and with whom they communicate from a device they carry than from more subjective sources, including what people say about themselves. In short, people lie—cell phones don’t.
Detecting Trends for the Common Good
Reality mining can also help city planners unravel traffic snarls and public health officials track and prevent the spread of illnesses, such as severe acute respiratory syndrome, or SARS
Wireless companies could use the information to help keep customers from switching to a rival.
One aim of meetings is to ensure participants interact with people from disparate backgrounds, and a planner can use radio-enabled tags to know whether that’s happening. “We try to intervene and change that by collecting data on how people are interacting.”
“I just think it can be put to better use to deliver services that are interesting or that help people.”