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Analysis of predictive spatio-temporal queries

Analysis of predictive spatio-temporal queries Given a set of objects S , a spatio-temporal window query q retrieves the objects of S that will intersect the window during the (future) interval q T . A nearest neighbor query q retrieves the objects of S closest to q during q T . Given a threshold d , a spatio-temporal join retrieves the pairs of objects from two datasets that will come within distance d from each other during q T . In this article, we present probabilistic cost models that estimate the selectivity of spatio-temporal window queries and joins, and the expected distance between a query and its nearest neighbor(s). Our models capture any query/object mobility combination (moving queries, moving objects or both) and any data type (points and rectangles) in arbitrary dimensionality. In addition, we develop specialized spatio-temporal histograms, which take into account both location and velocity information, and can be incrementally maintained. Extensive performance evaluation verifies that the proposed techniques produce highly accurate estimation on both uniform and non-uniform data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Database Systems (TODS) Association for Computing Machinery

Analysis of predictive spatio-temporal queries

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References (70)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2003 by ACM Inc.
ISSN
0362-5915
DOI
10.1145/958942.958943
Publisher site
See Article on Publisher Site

Abstract

Given a set of objects S , a spatio-temporal window query q retrieves the objects of S that will intersect the window during the (future) interval q T . A nearest neighbor query q retrieves the objects of S closest to q during q T . Given a threshold d , a spatio-temporal join retrieves the pairs of objects from two datasets that will come within distance d from each other during q T . In this article, we present probabilistic cost models that estimate the selectivity of spatio-temporal window queries and joins, and the expected distance between a query and its nearest neighbor(s). Our models capture any query/object mobility combination (moving queries, moving objects or both) and any data type (points and rectangles) in arbitrary dimensionality. In addition, we develop specialized spatio-temporal histograms, which take into account both location and velocity information, and can be incrementally maintained. Extensive performance evaluation verifies that the proposed techniques produce highly accurate estimation on both uniform and non-uniform data.

Journal

ACM Transactions on Database Systems (TODS)Association for Computing Machinery

Published: Dec 1, 2003

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