基于基窗口的多维数据流相关性分析算法  

Correlation Analysis on Multidimensional Data Streams Based on Base-Windows

作　　者：钱江波[1] 王志杰[1] 陈华辉[1] 董一鸿[1] 谢志军[1] 王永利[2] 

机构地区：[1]宁波大学信息科学与工程学院,宁波315211 [2]南京理工大学计算机科学与技术学院,南京210094

出　　处：《模式识别与人工智能》2012年第3期435-444,共10页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.60803021;61170035;60973047;60902097);浙江省公益技术应用研究项目(No.2010C33149);宁波市自然科学基金项目(No.2010A610115)资助

摘　　要：多维数据流相关性分析的研究较少,且主要集中在单一滑动窗口分析.文中提出一种基于基窗口的在线典型相关分析算法(Base_win_CCA).算法动态维护基窗口的统计量用于多维相关性分析,时空复杂度大为减少,并且可根据多用户并发请求获取多个窗口范围的相关性,较灵活,运算结果精确.理论分析和实验结果表明算法在基窗口越大,相关性查询窗口越大,数据流条数越多,查询用户越多的情况下能体现出优越的性能.Multidimensional data stream analysis is seldom studied, even the minor contribution is mainly from the analytical works on a single sliding window model. An on-line correlation analysis algorithm called Base_win_ CCA algorithm is presented, which significantly reduces space and time complexity by performing simultaneous correlation analysis on multidimensional data streams. Technically, the algorithm achieves the correlation of multiple windows in a flexible and accurate way by dynamically maintaining statistics data. Theoretical analysis and experimental results indicate that the proposed algorithm is remarkable in performance when the window is larger with sufficient data streams and users.

关 键 词：多维数据流 相关性分析 基窗口 统计量 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]