期刊文献+

分布式数据流上低通信开销的连续极值查询方法研究

Communication Reduction for Continuous Extreme Values Monitoring over Distributed Data Streams
下载PDF
导出
摘要 数据流本质上是分布的,很多时候必须考虑通信开销.基于滑动窗口模型,考虑分布式数据流上的精确连续极值查询问题,对降低通信开销的策略进行了研究.分析了滑动窗口和极值查询的特性,提出了一种数据裁剪策略,系统只需保存少量数据即可满足极值查询的需求,并从理论上证明了该裁剪是存储最优的.远程节点在保证全局结果正确性的前提下尽量延迟数据传递,从而尽可能对局部数据流进行裁剪过滤,达到降低通信量的目的.理论分析和实验结果证明了上述方法的有效性.
出处 《计算机研究与发展》 EI CSCD 北大核心 2007年第z3期61-66,共6页 Journal of Computer Research and Development
基金 国家"八六三"高技术研究发展计划基金项目(2006AA01Z451,2006AA10Z237) 国防预研基金项目
  • 相关文献

参考文献13

  • 1[1]B Babcock,et al.Models and issues in data stream systems.PODS,Madison,Wisconsin,2002
  • 2金澈清,钱卫宁,周傲英.流数据分析与管理综述[J].软件学报,2004,15(8):1172-1181. 被引量:161
  • 3[3]G Cormode,M Garofalakis.Streaming in a connected world:Querying and tracking distributed data streams.VLDB Tutorials,Seoul,Korea,2006
  • 4[4]S Madden,et al.TAG:A tiny aggregation service for ad-hoc sensor networks.Symposium on Operating System Design and Implementation,Boston,2002
  • 5[5]C Olston,et al.Adaptive precision setting for cached approximate values.SIGMOD,Berkeley,California,2001
  • 6[6]C Olston,J Jiang,J Widom.Adaptive filters for continuous queries over distributed data streams.ACM SIGMOD,San Diego,California,2003
  • 7[7]A Jain,et al.Adaptive stream resource management using Kalman filters.ACM SIGMOD,Paris,2004
  • 8[8]R E Kalman.A new approach to linear filtering and prediction problems.ASME-Journal of Basic Engineering,1960,82(D):35-45
  • 9[9]G Cormode,M Garofalakis.Sketching streams through the net:Distributed approximate query tracking.VLDB,Trondheim,Norway,2005
  • 10[11]S Madden,et al.Supporting aggregate queries over ad-hoc wireless sensor networks.IEEE Workshop on Mobile Computing Systems and Applications,Callicoon,New York,2002

二级参考文献52

  • 1Babcock B, Babu S, Datar M, Motwani R, Widom J. Models and issues in data streams. In: Popa L, ed. Proc. of the 21st ACM SIGACT-SIGMOD-SIGART Symp. on Principles of Database Systems. Madison: ACM Press, 2002. 1~16.
  • 2Terry D, Goldberg D, Nichols D, Oki B. Continuous queries over append-only databases. SIGMOD Record, 1992,21(2):321-330.
  • 3Avnur R, Hellerstein J. Eddies: Continuously adaptive query processing. In: Chen W, Naughton JF, Bernstein PA, eds. Proc. of the 2000 ACM SIGMOD Int'l Conf. on Management of Data. Dallas: ACM Press, 2000. 261~272.
  • 4Hellerstein J, Franklin M, Chandrasekaran S, Deshpande A, Hildrum K, Madden S, Raman V, Shah MA. Adaptive query processing: Technology in evolution. IEEE Data Engineering Bulletin, 2000,23(2):7-18.
  • 5Carney D, Cetinternel U, Cherniack M, Convey C, Lee S, Seidman G, Stonebraker M, Tatbul N, Zdonik S. Monitoring streams?A new class of DBMS applications. Technical Report, CS-02-01, Providence: Department of Computer Science, Brown University, 2002.
  • 6Guha S, Mishra N, Motwani R, O'Callaghan L. Clustering data streams. In: Blum A, ed. The 41st Annual Symp. on Foundations of Computer Science, FOCS 2000. Redondo Beach: IEEE Computer Society, 2000. 359-366.
  • 7Domingos P, Hulten G. Mining high-speed data streams. In: Ramakrishnan R, Stolfo S, Pregibon D, eds. Proc. of the 6th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. Boston: ACM Press, 2000. 71-80.
  • 8Domingos P, Hulten G, Spencer L. Mining time-changing data streams. In: Provost F, Srikant R, eds. Proc. of the 7th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. San Francisco: ACM Press, 2001. 97~106.
  • 9Zhou A, Cai Z, Wei L, Qian W. M-Kernel merging: Towards density estimation over data streams. In: Cha SK, Yoshikawa M, eds. The 8th Int'l Conf. on Database Systems for Advanced Applications (DASFAA 2003). Kyoto: IEEE Computer Society, 2003. 285~292.
  • 10Gibbons PB, Matias Y. Synopsis data structures for massive data sets. In: Tarjan RE, Warnow T, eds. Proc. of the 10th Annual ACM-SIAM Symp. on Discrete Algorithms. Baltimore: ACM/SIAM, 1999. 909-910.

共引文献160

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部