Date of Original Version
Copyright © 1995 by the VLDB Endowment. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by the permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.
Abstract or Description
We examine the estimation of selectivities for range and spatial join queries in real spatial databases. As we have shown earlier [FK94], real point sets: (a) violate consistentlythe "uniformity" and "independence" assumptions, (b) can often be described as "fractals", with non-integer (fractal) dimension. In this paper we show that, among the infinite family of fractal dimensions, the so called "Correlation Dimension" D2 is the one that we need to predict the selectivity of spatial join.
The main contribution is that, for all the real and synthetic point-sets we tried, the average number of neighbors for a given point of the point-set follows a power law, with D2 as the exponent. This immediately solves the selectivity estimation for spatial joins, as well as for "biased" range queries (i.e., queries whose centers prefer areas of high point density).
We present the formulas to estimate the selectivity for the biased queries, including an integration constant (Kshape!,) for each query shape. Finally, we show results on real and synthetic point sets, where our formulas achieve very low relative errors (typically about 10%, versus 40%-100% of the uniformity assumption).
Proceedings of 21th International Conference on Very Large Data Bases.