n-gram geo-trace modeling

  • Authors:

  • Senaka Buthpitiya;Ying Zhang;Anind K. Dey;Martin Griss

  • Affiliations:

  • Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
  • Year:
  • 2011

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Abstract

As location-sensing smart phones and location-based services gain mainstream popularity, there is increased interest in developing techniques that can detect anomalous activities. Anomaly detection capabilities can be used in theft detection, remote elder-care monitoring systems, and many other applications. In this paper we present an n-gram based model for modeling a user's mobility patterns. Under the Markovian assumption that a user's location at time t depends only on the last n - 1 locations until t - 1, we can model a user's idiosyncratic location patterns through a collection of n-gram geo-labels, each with estimated probabilities. We present extensive evaluations of the n-gram model conducted on real-world data, compare it with the previous approaches of using T-Patterns and Markovian models, and show that for anomaly detection the n-gram model outperforms existing work by approximately 10%. We also show that the model can use a hierarchical location partitioning system that is able to obscure a user's exact location, to protect privacy, while still allowing applications to utilize the obscured location data for modeling anomalies effectively.