1. 北京大学软件与微电子学院,北京,102600
2. 复旦大学计算机科学技术学院,上海,200433
3. 同济大学电信学院,上海,200092
4. 北京大学软件与微电子学院北京,102600
5. 复旦大学计算机科学技术学院上海,200433
6. 同济大学电信学院上海,200092
纸质出版:2009
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孙圣力, 戴东波, 黄震华, 等. 概率数据流上Skyline查询处理算法[J]. 电子学报, 2009,37(2):285-293.
SUN Sheng-li, DAI Dong-bo, HUANG Zhen-hua, et al. Algorithm on Computing Skyline over Probabilistic Data Stream[J]. Acta Electronica Sinica, 2009, 37(2): 285-293.
概率数据流管理与分析逐步引起了研究者们的关注.Skyline查询技术是近年来数据库领域的研究热点.此前相关工作仅限于静态数据集或传统确定性数据流上的Skyline查询处理
尚无人考虑概率数据流上的Skyline计算问题
本文提出的SOPDS算法则较好地解决了该问题.在采用适应性更强的网格索引的基础上
提出了概率定界、逐步求精、提前淘汰与选择补偿等启发式规则对算法从时间和空间两方面进行了系统地优化.实验表明
算法在时间与空间上具有较高的整体性能.
Management and analysis of uncertain
probabilistic data stream has attracted considerable attention within database community.Skyline query processing is an open question recently.Although previous work has addressed skyline computations over static data or traditional data stream
skyline computation over probabilistic data stream is still at large.We propose an efficient algorithm SOPDS to handle this issue.Based on more adaptable grid index
a set of heuristic rules like probability bounding
progressive refinement
pre-elimination and selective compensation are developed to improve the comprehensive performance of SOPDS from point of reducing both CPU overhead and memory consumption.Massive experiments demonstrate that SOPDS is of high overall performance.
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