Parallel algorithms for spatial data partition and join processing

The spatial join operations combine two sets of spatial data by their spatial relationships. They are among the most important, yet most time-consuming operations in spatial databases. We consider the problem of binary polygon intersection joins based on the filter-and-refine strategy. Our objective is to minimize the I/O cost and the response time for the refinement step. First, a graph model is proposed to formalize the refinement cost and matrix-based sequential data partition algorithms are introduced. Then a parallel data partitioning algorithm is developed with a detailed complexity analysis. Based on the data partition results, a distribution algorithm is also proposed for scheduling parallel spatial join processing.