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计算机工程 ›› 2009, Vol. 35 ›› Issue (2): 68-69,7. doi: 10.3969/j.issn.1000-3428.2009.02.025

• 软件技术与数据库 • 上一篇    下一篇

基于分区技术的静态R树索引并行计算技术

周 芹1,2,钟耳顺1,黄耀欢3   

  1. (1. 中国科学院地理科学与资源研究所,北京 100101;2. 中国科学院研究生院,北京 100039;3. 中国水利水电科学研究院,北京 100044)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-01-20 发布日期:2009-01-20

Parallel Computation Technique for Static R-tree Index Based on Partition Technology

ZHOU Qin1,2, ZHONG Er-shun1, HUANG Yao-huan3   

  1. (1. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101; 2. Graduate University of Chinese Academy of Sciences, Beijing 100039; 3. China Institute of Water Resources and Hydropower Research, Beijing 100044)
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-01-20 Published:2009-01-20

摘要: 海量空间数据静态R树索引的加载时耗很大。该文利用关系数据库的优势,以空间数据分区存储技术为基础,提出针对自上而下的贪婪分裂算法的静态R树并行加载方法。该方法提高了海量数据批量加载效率,支持分区粒度的索引重建。论证与实验结果表明,并行构建的R树在合理空间数据分区下可以获得更高查询效率。

关键词: 空间索引, 静态R树, 分区, 并行计算

Abstract: Bulk-loading of static R-tree index for massive spatial data is time consuming. This paper utilizes the advantage of relational database. Aiming at the Top-down Greedy-Split(TGS) algorithm, it proposes parallel bulk-loading method of static R-tree based on the storage technology of spatial data. This method accelerates the mass data bulk loading efficient, and supports the index rebuild of partition grading. Argumentation and experimental results show that the parallel built R-tree has higher query efficiency under reasonable spatial data partition.

Key words: spatial index, static R-tree, partition, parallel computation

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