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Indexing Multi-Dimensional Trajectories for Similarity Queries
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Author(s): Michail Valachos (IBM T.J. Watson Research Center, USA), Marios Hadjieleftheriou (University of California-Riverside, USA), Eamonn Keogh (University of California - Riverside, USA)and Dimitrios Gunopulos (University of California - Riverside, USA)
Copyright: 2005
Pages: 23
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.ch005
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Abstract
With the abundance of low-cost storage devices, a plethora of applications that store and manage very large multi-dimensional trajectories (or time-series) datasets have emerged recently. Examples include traffic supervision systems, video surveillance applications, meteorology and more. Thus, it is becoming essential to provide a robust trajectory indexing framework designed especially for performing similarity queries in such applications. In this regard, this chapter presents an indexing scheme that can support a wide variety of (user-customizable) distance measures while, at the same time, it guarantees retrieval of similar trajectories with accuracy and efficiency.
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