Abstract

In recent years, there have been needs of accessing spatial data from distributed and preexisting spatial database systems interconnected through a network. In a distributed environment, spatial joins for two spatial relations residing at geographically separated sites are expensive in terms of computation and transmission cost because of the large size and complexity of spatial data. Previous distributed algorithm based on the spatial semijoin has accomplished performance improvements by eliminating objects before transmission to reduce both transmission and local processing costs. But with a widespread of a high bandwidth data transmission, the parallelism through data redistribution may improve the performance of spatial joins in spite of additional transmission costs. Hence, we propose a parallel spatial join processing for distributed spatial databases. We apply the task distribution method minimizing the data transmission and the solution for task distribution using a graph partitioning method. In experiments, we showed that the proposed method provides useful reductions in the cost of evaluating a join.

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