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Flock Pattern Queries

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Spatio-Temporal Databases

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

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Abstract

With the recent advancements and wide usage of location detection devices, large quantities of data are collected, for example, by GPS and cellular technologies in the form of trajectories. While most previous work on trajectory-based queries has concentrated on traditional range, nearest-neighbor, similarity queries, and clustering techniques, there is an increasing interest in queries that capture the “aggregate” behavior of trajectories as groups. Consider, for example, finding groups of moving objects that move “together”, i.e., within a predefined distance to each other for a continuous period of time. Such type of queries typically arises in many surveillance and monitoring applications, e.g. identify groups of suspicious people, convoys of vehicles, flocks of animals. In this chapter, we first show that the on-line flock discovery problem is polynomial, and then we propose a framework and several strategies to discover such patterns in streaming spatio-temporal data. Experiments with real and synthetic trajectorial datasets show that the proposed algorithms are efficient and scalable under different conditions.

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Authors and Affiliations

  1. IBM Research Laboratory Brazil, Rio de Janeiro, RJ, Brazil

    Marcos R. Vieira

  2. Department of Computer Science and Engineering, Bourns College of Engineering, University of California, Riverside, CA, USA

    Vassilis J. Tsotras

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  1. Marcos R. Vieira
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  2. Vassilis J. Tsotras
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Correspondence to Marcos R. Vieira .

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Vieira, M.R., Tsotras, V.J. (2013). Flock Pattern Queries. In: Spatio-Temporal Databases. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-02408-0_4

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  • DOI: https://doi.org/10.1007/978-3-319-02408-0_4

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  • Online ISBN: 978-3-319-02408-0

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