关键词: 时空数据流, 移动对象, 空间索引, R树, 对象聚合

Abstract: In light of the characteristics of spatio-temporal data stream,we propose a moving object spatial index construction method called HSTRCL,which is based on time window data sorting and bulk loading.It segments the continuous spatio-temporal data stream with fixed-length time windows; after finishing caching the data of a time window,by combining parallel processing and optimized bulk loading technology,we isolate as much as possible the time-consuming work of data partitioning and sorting operations from the traditional build process,and parallize them with the reception of data streams and other build operations.Furthermore,we avoid unnecessary locking synchronization overhead.And all these techniques improve the efficiency of index construction.In addition,to meet the performance and diverse query requirements,we also adopt the primary-auxiliary index construction technology based on Hash and STR.To further improve the performance in the object query scenario,we invent another moving object spatial index construction method OAHSTRCL via time window object aggregation and bulk loading,where objects are divided more finely,and the object query time required is about 65% of HSTRCL,though it will affect the performance of spatial query to some extent.Theoretical analysis and experiments have demonstrated the effectiveness of our proposed methods.

Key words: spatio-temporal data stream, moving object, spatial index, R-tree, object aggregation