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Mining in Spatio-Temporal Databases

Mining in Spatio-Temporal Databases
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Author(s): Junmei Wang (National University of Singapore, Singapore), Wynne Hsu (National University of Singapore, Singapore) and Mong Li Lee (National University of Singapore, Singapore)
Copyright: 2005
Pages: 22
Source title: Spatial Databases: Technologies, Techniques and Trends
Source Author(s)/Editor(s): Yannis Manalopoulos (Aristotle University of Thessaloniki, Greece), Apostolos Papadopoulos (Aristotle University of Thessaloniki, Greece) and Michael Gr. Vassilakopoulos (Technological Educational Institute of Thessaloniki, Greece)
DOI: 10.4018/978-1-59140-387-6.ch012

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Abstract

Recent interest in spatio-temporal applications has been fueled by the need to discover and predict complex patterns that occur when we observe the behavior of objects in the three-dimensional space of time and spatial coordinates. Although the complex and intrinsic relationships among the spatio-temporal data limit the usefulness of conventional data mining techniques to discover the patterns in the spatio-temporal databases, they also lead to opportunities for mining new classes of patterns in spatio-temporal databases. This chapter provides a survey of the work done for mining patterns in spatial databases and temporal databases, and the preliminary work for mining patterns in spatio-temporal databases. We highlight the unique challenges of mining interesting patterns in spatio-temporal databases. We also describe two special types of spatio-temporal patterns: location-sensitive sequence patterns and geographical features for location-based service patterns.

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