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PERVASIVE
2011
Springer

n-Gram Geo-trace Modeling

7 years 9 months ago
n-Gram Geo-trace Modeling
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 ngram 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 syst...
Senaka Buthpitiya, Ying Zhang, Anind K. Dey, Marti
Added 17 Sep 2011
Updated 17 Sep 2011
Type Journal
Year 2011
Where PERVASIVE
Authors Senaka Buthpitiya, Ying Zhang, Anind K. Dey, Martin L. Griss
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