Research on the Distributed Spatial Topological Query Optimization Algorithm

  • Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China

Received date: 2013-05-15

  Revised date: 2013-08-24

  Online published: 2013-09-29


Due to complex data structure, complicated spatial relationship and massive data volume, distributed spatial query is a time-consuming processing, which will cause high transmission and processing cost. Query processing method in traditional distributed database cannot satisfy the demands of query in distributed geospatial database. Therefore, new query methods in distributed geospatial database need to be studied. In this paper, the distributed spatial join query processing is deeply studied based on the existing optimizing methods of the conventional query processing in traditional distributed database, and a series of transformation rules of relational algebra expression based on cross-border topological join optimization rules are proposed. The processed query tree is optimized by equivalent transformation after data localization. The global optimized method of distributed spatial join query for different fragments is studied. The global spatial query can be transformed into some local fragments joins effectively. The spatial join query is processed in the local area, avoiding the data transmission of spatial data among data nodes during the processing of query, so that the query performance can be improved. To improve the efficiency of the method, some new concepts were put forward, including query merged tree and execution plan tree, which can optimize the executing path of query plan. For example, by adjusting the executing order, some processes with low cost execute first, and the time-consuming processes execute based on the result set generated by the previous processes so as to reduce the process of time-consuming parts and resolve the problem of high cost of query processing to improve the performance of distributed spatial query. The experiment based on the vector data of China shows our methods can reduce the cost of the spatial join and data transmission among the nodes, and the performance improve 28.5%, which demonstrates that our methods outperform the traditional methods in terms of both algorithm complexity and the running time.

Cite this article

YANG Dian-Hua . Research on the Distributed Spatial Topological Query Optimization Algorithm[J]. Journal of Geo-information Science, 2013 , 15(5) : 643 -648,679 . DOI: 10.3724/SP.J.1047.2013.00643


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