Efficient computation of spatial joins with intersection predicates

Abstract

We introduce a new algorithm to compute the spatial join of two or more spatial data sets, when indexes are not available on them. Size Separation Spatial Join (S³ J) imposes a hierarchical decomposition of the data space and, in contrast to previous approaches, requires no replication of entities from the input data sets. Thus its execution time depends only on the sizes of the joined data sets. We described S³ J and present an analytical evaluation of its I/O and processor requirements comparing them with those of previously proposed algorithms for the same problem. We show that S³ J has relatively simple cost estimation formulas that can be exploited by a query optimizer. S³ J can be efficiently implemented using software already present in many relational systems. In addition, we introduce Dynamic Spatial Bitmaps (DSB), a new technique that enables S³ J dynamically or statically to exploit bitmap query processing techniques. Finally, we present experimental results for a prototype implementation of S³ J involving real and synthetic data sets for a variety of data distributions. Our experimental results are consistent with our analytical observations and demonstrate the performance benefits of S³ J over alternative approaches that have been proposed recently.W