An Efficient Cost Model for Spatial Joins Using R-trees

Spatial join is one of the fundamental operations in a Spatial Data Base Management System. Recently, the family of R-tree-based data structures has been adopted to support the execution of spatial joins. This paper introduces an analytical model that efficiently estimates the cost (in terms of disk accesses) of a spatial join query between two spatial datasets. The proposed model is based on an analytical formula that estimates the cost of the range query using Rtrees. In addition, comparison results are presented which show the accuracy of the analytical estimations when compared to actual tests on both synthetic and real datasets. It turns out that the relative error rarely exceeds 15% for all combinations.