基于时空分区聚簇的海量时空数据性能优化方法研究

A Study of Performance Optimization Method for Massive Spaito-temporal Data Based on Spatio-temporal Partition Clustering

时空一体化的海量数据管理及相应的时序分析能力是新一代G IS软件体系的重要研究目标之一。当前,基于无缝海量大表的空间及时态空间数据的存取效率亟待提高。为了对海量时空数据进行有效管理和提高时空检索效率,以扩充关系型时空模型为基础,对大型对象-关系型数据库平台所提供的数据分区与聚簇方法进行了时空维的扩展,提出了基于时空分区聚簇(spatio-temporal partition c lustering,STPC)的海量时空数据性能优化方法。基于2GB^60GB的单表所进行的检索效率对比测试结果表明,STPC机制较普通的数据组织方式时空检索效率平均提高了10.1%。

Integrative management of massive spatio-temporal data and the relevant capabilities for time series analysis are important research targets of new generation GIS software architecture. At present, access efficiency of spatial and spatiotemporal data based on seamless massive table mechanism needs solid improvement. For managing massive spatio-temporal data effectively and improving spatio-temporal search performance, partitioning and clustering method of large objectrelational database platform are extended to spatio-temporal dimension, and performance optimization method for massive spaito-temporal data based on spatio-temporal partition clustering is presented, which is based on extended relational spatiotemporal data model. The search efficiency test on single table of 2GB to 60GB shows that STPC provides better spaitotemporal search performance（about 10. 1% ） than the normal data organization methods.