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Mobility Database Management

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Mobility Data Management and Exploration

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Abstract

Adding temporal information, as an extra attribute in spatial databases, is not as straightforward as it may appear at a first glance. Time is not yet another dimension besides the two (or three, in some applications) spatial dimensions; monotonicity, for example, is a key difference. Could we adopt “as-is” methods and techniques for spatial databases, such as the ones outlined in Chap. 2? The answer is rather not, and this has been argued extensively in the spatiotemporal database literature. Therefore, novel data types (e.g., moving points), query processing techniques (e.g., “search for trajectories that ‘entered’ an area during a timeframe” or “search for trajectories that are ‘similar’ with respect to a reference trajectory”) and indexing methods (most probably, extensions of the well-known R-tree) have been explored. This chapter surveys the above aspects, which are essential components of a database system targeting at efficiently handing mobility data. In particular, interesting location- and mobility-aware queries are overviewed. Then, at physical level, selected indexing and query processing techniques that have been designed to efficiently support the above models and query types are presented.

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Authors and Affiliations

  1. Department of Statistics and Insurance Science, University of Piraeus, Piraeus, Greece

    Nikos Pelekis

  2. Department of Informatics, University of Piraeus, Piraeus, Greece

    Yannis Theodoridis

Authors
  1. Nikos Pelekis
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  2. Yannis Theodoridis
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Pelekis, N., Theodoridis, Y. (2014). Mobility Database Management. In: Mobility Data Management and Exploration. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0392-4_4

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  • DOI: https://doi.org/10.1007/978-1-4939-0392-4_4

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  • Online ISBN: 978-1-4939-0392-4

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