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267
Reality Mining: Sensing Complex Social Systems
- J. OF PERSONAL AND UBIQUITOUS COMPUTING
, 2005
"... We introduce a system for sensing complex social systems with data collected from one hundred mobile phones over the course of six months. We demonstrate the ability to use standard Bluetooth-enabled mobile telephones to measure information access and use in different contexts, recognize social patt ..."
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Cited by 718 (27 self)
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We introduce a system for sensing complex social systems with data collected from one hundred mobile phones over the course of six months. We demonstrate the ability to use standard Bluetooth-enabled mobile telephones to measure information access and use in different contexts, recognize social patterns in daily user activity, infer relationships, identify socially significant locations, and model organizational rhythms.
Learning and inferring transportation routines
, 2004
"... This paper introduces a hierarchical Markov model that can learn and infer a user’s daily movements through the community. The model uses multiple levels of abstraction in order to bridge the gap between raw GPS sensor measurements and high level information such as a user’s mode of transportation ..."
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Cited by 312 (22 self)
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This paper introduces a hierarchical Markov model that can learn and infer a user’s daily movements through the community. The model uses multiple levels of abstraction in order to bridge the gap between raw GPS sensor measurements and high level information such as a user’s mode of transportation or her goal. We apply Rao-Blackwellised particle filters for efficient inference both at the low level and at the higher levels of the hierarchy. Significant locations such as goals or locations where the user frequently changes mode of transportation are learned from GPS data logs without requiring any manual labeling. We show how to detect abnormal behaviors (e.g. taking a wrong bus) by concurrently tracking his activities with a trained and a prior model. Experiments show that our model is able to accurately predict the goals of a person and to recognize situations in which the user performs unknown activities.
Sensing meets mobile social networks: The design, implementation and evaluation of the CenceMe application
- in Proceedings of the International Conference on Embedded Networked Sensor Systems (SenSys
, 2008
"... We present the design, implementation, evaluation, and user experiences of the CenceMe application, which represents the first system that combines the inference of the presence of individuals using off-the-shelf, sensor-enabled mobile phones with sharing of this information through social networkin ..."
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Cited by 252 (19 self)
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We present the design, implementation, evaluation, and user experiences of the CenceMe application, which represents the first system that combines the inference of the presence of individuals using off-the-shelf, sensor-enabled mobile phones with sharing of this information through social networking applications such as Facebook and MySpace. We discuss the system challenges for the development of software on the Nokia N95 mobile phone. We present the design and tradeoffs of split-level classification, whereby personal sensing presence (e.g., walking, in conversation, at the gym) is derived from classifiers which execute in part on the phones and in part on the backend servers to achieve scalable inference. We report performance measurements that characterize the computational requirements of the software and the energy consumption of the CenceMe phone client. We validate the system through a user study where twenty two people, including undergraduates, graduates and faculty, used CenceMe continuously over a three week period in a campus town. From this user study we learn how the system performs in a production environment and what uses people find for a personal sensing system.
Mining interesting locations and travel sequences from gps trajectories
- In Proc. of 2009 Int. World Wide Web Conf. (WWW’09
, 2009
"... The increasing availability of GPS-enabled devices is changing the way people interact with the Web, and brings us a large amount of GPS trajectories representing people’s location histories. In this paper, based on multiple users ’ GPS trajectories, we aim to mine interesting locations and classica ..."
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Cited by 168 (18 self)
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The increasing availability of GPS-enabled devices is changing the way people interact with the Web, and brings us a large amount of GPS trajectories representing people’s location histories. In this paper, based on multiple users ’ GPS trajectories, we aim to mine interesting locations and classical travel sequences in a given geospatial region. Here, interesting locations mean the culturally important places, such as Tiananmen Square in Beijing, and frequented public areas, like shopping malls and restaurants, etc. Such information can help users understand surrounding locations, and would enable travel recommendation. In this work, we first model multiple individuals ’ location histories with a tree-based hierarchical graph (TBHG). Second, based on the TBHG, we propose a HITS (Hypertext Induced Topic Search)-based inference model, which regards an
Eigenbehaviors: Identifying Structure in Routine
- IN PROC. OF UBICOMP’06
, 2006
"... In this work we identify the structure inherent in daily human behavior with models that can accurately analyze, predict and cluster multimodal data from individuals and groups. We represent this structure by the principal components of the complete behavioral dataset, a set of characteristic vecto ..."
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Cited by 144 (7 self)
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In this work we identify the structure inherent in daily human behavior with models that can accurately analyze, predict and cluster multimodal data from individuals and groups. We represent this structure by the principal components of the complete behavioral dataset, a set of characteristic vectors we have termed eigenbehaviors. In our model, an individual’s behavior over a specific day can be approximated by a weighted sum of his or her primary eigenbehaviors. When these weights are calculated halfway through a day, they can be used to predict the day’s remaining behaviors with a 79 % accuracy for our test subjects. Additionally, we show that users of a similar demographic can be clustered into a “behavior space ” spanned by a set of their aggregate eigenbehaviors. These behavior spaces make it possible to determine the behavioral similarity between both individuals and groups, enabling 96 % classification accuracy of group affiliations. This approach capitalizes on the large amount of rich data previously captured during the Reality Mining study from mobile phones continuously logging location, proximate people, and communication of 100 subjects at MIT over the course of nine months.
Inference attacks on location tracks
- In Pervasive
, 2007
"... Abstract. Although the privacy threats and countermeasures associated with location data are well known, there has not been a thorough experiment to assess the effectiveness of either. We examine location data gathered from volunteer subjects to quantify how well four different algorithms can identi ..."
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Cited by 144 (5 self)
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Abstract. Although the privacy threats and countermeasures associated with location data are well known, there has not been a thorough experiment to assess the effectiveness of either. We examine location data gathered from volunteer subjects to quantify how well four different algorithms can identify the subjects ’ home locations and then their identities using a freely available, programmable Web search engine. Our procedure can identify at least a small fraction of the subjects and a larger fraction of their home addresses. We then apply three different obscuration countermeasures designed to foil the privacy attacks: spatial cloaking, inaccuracy, and imprecision. We show how much obscuration is necessary to maintain the privacy of all the subjects.
Location-Based Activity Recognition using Relational Markov Networks
"... In this paper we define a general framework for activity recognition by building upon and extending Relational Markov Networks. Using the example of activity recognition from location data, we show that our model can represent a variety of features including temporal information such as time of day, ..."
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Cited by 144 (14 self)
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In this paper we define a general framework for activity recognition by building upon and extending Relational Markov Networks. Using the example of activity recognition from location data, we show that our model can represent a variety of features including temporal information such as time of day, spatial information extracted from geographic databases, and global constraints such as the number of homes or workplaces of a person. We develop an efficient inference and learning technique based on MCMC. Using GPS location data collected by multiple people we show that the technique can accurately label a person’s activity locations. Furthermore, we show that it is possible to learn good models from less data by using priors extracted from other people’s data.
A Survey of Computational Location Privacy
- PERSONAL AND UBIQUITOUS COMPUTING
, 2008
"... This is a literature survey of computational location privacy, meaning computation-based privacy mechanisms that treat location data as geometric information. This definition includes privacy-preserving algorithms like anonymity and obfuscation as well as privacy-breaking algorithms that exploit the ..."
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Cited by 120 (1 self)
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This is a literature survey of computational location privacy, meaning computation-based privacy mechanisms that treat location data as geometric information. This definition includes privacy-preserving algorithms like anonymity and obfuscation as well as privacy-breaking algorithms that exploit the geometric nature of the data. The survey omits non-computational techniques like manually inspecting geotagged photos, and it omits techniques like encryption or access control that treat location data as general symbols. The paper reviews studies of peoples’ attitudes about location privacy, computational threats on leaked location data, and computational countermeasures for mitigating these threats.
Extracting places and activities from gps traces using hierarchical conditional random fields
- International Journal of Robotics Research
, 2007
"... Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. We show how to extract a person’s activities and significant places from traces of GPS data. Our system uses hierarchically structured conditional random fields to generate a consistent mod ..."
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Cited by 119 (3 self)
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Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. We show how to extract a person’s activities and significant places from traces of GPS data. Our system uses hierarchically structured conditional random fields to generate a consistent model of a person’s activities and places. In contrast to existing techniques, our approach takes high-level context into account in order to detect the significant places of a person. Our experiments show significant improvements over existing techniques. Furthermore, they indicate that our system is able to robustly estimate a person’s activities using a model that is trained from data collected by other persons. 1
Extracting Places from Traces of Locations
, 2005
"... this paper, we describe an algorithm for extracting significant places from a trace of coordinates. Furthermore, we experimentally evaluate the algorithm with real, long-term data collected from three participants using a Place Lab client [15], a software client that computes location coordinates ..."
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Cited by 112 (4 self)
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this paper, we describe an algorithm for extracting significant places from a trace of coordinates. Furthermore, we experimentally evaluate the algorithm with real, long-term data collected from three participants using a Place Lab client [15], a software client that computes location coordinates by listening for RF-emissions from known radio beacons in the environment (e.g. 802.11 access points, GSM cell towers)
