2011 | OriginalPaper | Buchkapitel
n-Gram Geo-trace Modeling
verfasst von : Senaka Buthpitiya, Ying Zhang, Anind K. Dey, Martin Griss
Erschienen in: Pervasive Computing
Verlag: Springer Berlin Heidelberg
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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.