A VOMR-TREE BASED PARALLEL RANGE QUERY METHOD on DISTRIBUTED SPATIAL DATABASE

Abstract

Spatial index impacts upon the efficiency of spatial query seriously in distributed spatial database. In this paper, we introduce a parallel spatial range query algorithm, based on VoMR-tree index, which incorporates Voronoi diagrams into MR-tree, benefiting from the nearest neighbors. We first augments MR-tree to store the nearest neighbors and constructs the VoMR-tree index by Voronoi diagram. We then propose a novel range query algorithm based on VoMR-tree index. In processing a range query, we discuss the data partition method so that we can improve the efficiency by parallelization in distributed database. Just then a verification strategy is promoted. We show the superiority of the proposed method by extensive experiments using data sets of various sizes. The experimental results reveal that the proposed method improves the performance of range query processing up to three times in comparison with the widely-used R-tree variants.