An Improved Algorithm on Skyline Query over Probabilistic Data Stream
SOPDS is a kind of skyline query algorithm over probabilistic data stream. Based on grid index, a set of heuristic rules like probability bounds, progressive refinement, pre-elimination and selective compensation are devel- oped to improve the comprehensive performance of SOPDS on both CPU overhead and memory consumption. Through the analysis of the dominance relationship between uncertain objects, more effective filtering strategy and object iden- tity decision rule are added to SOPDS. And SOPDS is improved to a novel algorithm, ISOPDS. The experimental results show that ISOPDS could reduce the response time of skyline query effectively.
probabilistic data streamuncertain dataskylinecontinuous query
NETL
NSTL
万方数据
维普
