Efficient query processing on large spatial databases: A performance study

Abstract

A spatial index, called xBR+-tree, is compared against the R*-tree and the R+-tree.Performance of tree building and processing of common spatial queries is studied.Existing and new algorithms for processing these queries are utilized.Extensive experimental results on medium and large datasets are presented.The xBR+-tree is the winner of execution-time in all cases and of I/0 in most cases. Processing of spatial queries has been studied extensively in the literature. In most cases, it is accomplished by indexing spatial data using spatial access methods. Spatial indexes, such as those based on the Quadtree, are important in spatial databases for efficient execution of queries involving spatial constraints and objects. In this paper, we study a recent balanced disk-based index structure for point data, called xBR+-tree, that belongs to the Quadtree family and hierarchically decomposes space in a regular manner. For the most common spatial queries, like Point Location, Window, Distance Range, Nearest Neighbor and Distance-based Join, the R-tree family is a very popular choice of spatial index, due to its excellent query performance. For this reason, we compare the performance of the xBR+-tree with respect to the R*-tree and the R+-tree for tree building and processing the most studied spatial queries. To perform this comparison, we utilize existing algorithms and present new ones. We demonstrate through extensive experimental performance results (I/O efficiency and execution time), based on medium and large real and synthetic datasets, that the xBR+-tree is a big winner in execution time in all cases and a winner in I/O in most cases.