Effective Processing of Constraints based Spatial Join using R-Trees
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Copyright: © 2020 R. Parvathi and S. Palaniammal. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Problem statement: This study focuses on the spatial join effects with the constraints-based spatial data without any extra cost and Finding the minimum execution time of the spatial query and spatial selection method. Approach: Spatial joins are used to combine the spatial objects. The efficient processing depends upon the spatial queries. The execution time and 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 onjects. (3) We use scales with the shapes for spatial distribution. We also present several techniques for improving its execution time with respect to the 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. Results: The R*-tree concept reduce the number of search pages to combine spatial objects. By using this, CPU utilization time increases, the number of comparisons of spatial objects can be reduced and also reduces the I/O time. Conclusion/Recommendations: 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.
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- Spatial data mining
- spatial clustering
- spatial queries
- spatial join
- Minimum Bounding Rectangles (MBR)
- Akaike Information Criterion (AIC)
- real data
- constraints based