Indexing Multi-Dimensional Trajectories for Similarity Queries

Indexing Multi-Dimensional Trajectories for Similarity Queries

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
DOI: 10.4018/978-1-59140-387-6.ch005

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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