Spatial data appear in numerous applications, such as GIS, multimedia and even traditional databases. Most of the analysis has focused on point data, typically using the uniformity assumption, or, more accurately, a fractal distribution. However, no results exist for non-point spatial data, like 2-d regions (e.g., 3-d volumes (e.g., physical objects in the real world), etc.

This is exactly the problem we solve in this paper. Based on experimental evidence that real areas and volumes follow a "power law", that we named REGAL (REGion Area Law), we show (a) the theoretical implications of our model and its connection with the ubiquitous fractals and (b) the first of its practical uses, namely the selectivity estimation for range queries. Experiments on a variety of real datasets (islands, lakes, human-inhabited areas) show that our method is extremely accurate, enjoying a maximum relative error ranging from 1 to 5%, versus 30-70% of a naive model that uses the uniformity assumption.

22 pages

*On leave from Dipartimento di Matematica Pura ed Applicata, University of L'Aquila, Via Vetoio, I-67010, Italy.

Return to: SCS Technical Report Collection
School of Computer Science homepage

This page maintained by reports@cs.cmu.edu