Wavelet-Based Cost Estimation for Spatial Queries

  • Authors:

  • Min Wang;Jeffrey Scott Vitter;Lipyeow Lim;Sriram Padmanabhan

  • Affiliations:

  • -;-;-;-

  • Venue:
  • SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
  • Year:
  • 2001

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Abstract

Query cost estimation is an important and well-studied problem in relational database systems. In this paper we study the cost estimation problem in the context of spatial database systems.We introduce a new method that provides accurate cost estimation for spatial selections, or window queries, by building wavelet-based histograms for spatial data. Our method is based upon two techniques: (a) A representation transformation in which geometric objects are represented by points in higher-dimensional space and window queries correspond to semi-infinite range-sum queries, and (b) Multiresolution wavelet decomposition that provides a time-efficient and space-efficient approximation of the underlying distribution of the multidimensional point data, especially for semi-infinite range-sum queries. We also show for the first time how a wavelet decomposition of a dense multidimensional array derived from a sparse array through a partial-sum computation can still be computed efficiently using sparse techniques by doing the processing implicitly on the original data. Our method eliminates the drawbacks of the partition-based histogram methods in previous work, and even with very small space allocation it gives excellent cost estimation over a broad range of spatial data distributions and queries.