计算机科学 ›› 2010, Vol. 37 ›› Issue (3): 152-155169.

• 软件工程与数据库技术 • 上一篇    下一篇

一种适应性的流式数据聚集计算方法

侯东风,刘青宝,张维明,邓苏   

  1. (国防科学技术大学信息系统与管理学院 长沙410073)
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金(70771110)资助。

Adaptive Method of Computing Data Stream Aggregation

HOU Dong-feng,LIU Qing-bao,ZHANG Wei-ming, DENG Su   

  • Online:2018-12-01 Published:2018-12-01

摘要: 针对流式数据聚集查询问题,提出了一种基于适应性层次聚集树的计算方法。适应性层次聚集树结构基于多层次时间窗口模型,将距离当前时刻较近的数据保存为细粒度数据,而相对久远的数据仅保留高层聚集信息;适应性层次聚集树中粒度的划分取决于相应时间间隔的数据密度。稀疏密度的时间间隔对应粗粒度的划分,而高密度的间隔对应细粒度的划分。并且提出了相应的构建维护以及聚集查询计算方法。实验结果表明,该方法在非均匀分布条件下的流式数据聚集计算中具有较为明显的优势。

关键词: 流式数据,聚集计算,适应性层次聚集树,时间窗口

Abstract: A method based on Adaptive Hierarchy Aggregation tree(AHA-Tree) was presented for computing aggregalion of data stream. The tructure of AHA-Tree borrowed the idea of multiple time granularities hierarchical window model,the recent data was kept in fine granularity and the older in rough. In addition,the partition of granularities was determined by density of time unit, the sparse time unit was kept in rough granularity and the denseness in fine. Moreover, the method of maintenance and aggregate computing was proposed for aggregation query. Experiment shows that the method is efficient in processing the data under non-uniform distribution.

Key words: Data stream, Aggregate computation, Adaptive hierarchy aggregation tree, Time window

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