n-Gram Geo-trace Modeling

  • Senaka Buthpitiya
  • Ying Zhang
  • Anind K. Dey
  • Martin Griss
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6696)

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Senaka Buthpitiya
    • 1
  • Ying Zhang
    • 1
  • Anind K. Dey
    • 1
  • Martin Griss
    • 1
  1. 1.Carnegie Mellon UniversityPittsburghUSA

Personalised recommendations