Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/175

Selectivity estimation for predictive spatio-temporal queries

Authors Tao, Y.
Sun, J.
Papadias, D.
Issue Date 2003
Source Proceedings - International Conference on Data Engineering , 2003, p. 417-428
Summary This paper proposes a cost model for selectivity estimation of predictive spatio-temporal window queries. Initially, we focus on uniform data proposing formulae that capture both points and rectangles, and any type of object/query mobility combination (i.e., dynamic objects, dynamic queries or both). Then, we apply the model to non-uniform datasets by introducing spatio-temporal histograms, which in addition to the spatial, also consider the velocity distributions during partitioning. The advantages of our techniques are (i) high accuracy (1-2 orders of magnitude lower error than previous techniques), (ii) ability to handle all query types, and (iii) efficient handling of updates.
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Language English
Format Conference paper
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