Spatial joins are used to combine the spatial objects. The efficient processing depends upon the spatial queries. The execution time and input/output (I/O) time of spatial queries are crucial, because the spatial objects are very large and have several relations. In this article, we use several techniques to improve the efficiency of the spatial join; 1. We use R*-trees for spatial queries since R*-trees are very suitable for supporting spatial queries as it is one of the efficient member of R-tree family; 2. The different shapes namely point, line, polygon and rectangle are used for isolating and clustering the spatial objects; 3. We use scales with the shapes for spatial distribution. We also present several techniques forimproving its execution time with respect to the central processing unit (CPU) and I/O-time. In the proposed constraints based spatial join algorithm, total execution time is improved compared with the existing approach in order of magnitude. Using a buffer of reasonable size, the I/O time is optimal. The performance of the various approaches is investigated with the synthesized and real data set and the experimental results are compared with the large data sets from real applications.
Key words: Spatial data mining, spatial clustering, spatial queries, spatial join
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