ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume I-2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-2, 37–43, 2012
https://doi.org/10.5194/isprsannals-I-2-37-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-2, 37–43, 2012
https://doi.org/10.5194/isprsannals-I-2-37-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  11 Jul 2012

11 Jul 2012

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

Z. Fu2,1 and S. Liu1 Z. Fu and S. Liu
  • 1School of Remote and Sensing , Wuhan University, Wuhan, China
  • 2State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, China

Keywords: Range Query, Parallel Computing, Distributed Spatial Database, Spatial Index, VoMR-tree

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.