基于对象异常频度的数据流阈值查询  

Abnormal Frequency Threshold Queries for Objects over Data Stream

作　　者：张敬伟[1,2] 金澈清[1] 林煜明[1,2] 周傲英[1] 

机构地区：[1]华东师范大学软件学院上海市高可信计算重点实验室,上海200062 [2]桂林电子科技大学广西可信软件重点实验室,广西桂林541004

出　　处：《计算机学报》2013年第8期1602-1615,共14页Chinese Journal of Computers

基　　金：国家"九七三"重点基础研究发展规划项目基金(2012CB316203);国家自然科学基金项目(60933001;61070052;60925008;61021004);广西自然科学基金(2012GXNSFBA053171);广西可信软件重点实验室主任基金课题(kx201203)资助

摘　　要：数据流查询与处理技术广泛应用在金融系统等诸多领域中,银行卡交易系统中存在的由终端复用所导致的不规范交易即是一个典型案例.此类不规范交易存在两个特点:(1)对象频繁出现;(2)对象的值序列频繁波动.然而,现有的频繁项挖掘算法仅考虑第一个特点,无法有效解决问题,亟需开发新技术进行检测.文中首先形式化地定义了这类不规范交易查询,再设计了多个创新算法进行处理.此外,文中还将工作扩展到滑动窗口模型以处理数据流演化问题.理论分析与实验结果均表明所提方法具有较佳性能,空间复杂度与时间复杂度都较低.Querying and processing data streams are widely used in many applications,such as financial systems and so on.For example,in bank card transaction systems,there exist abnormal trading records caused by terminal multiplexers.In general,such records often contain many abnormal objects that occur frequently with high fluctuating rate.However,existing work on frequent item mining cannot be used to handle this issue directly since only the item’s frequency,not the fluctuating rate,is considered.In this paper,we first define the query formally,and then propose several novel solutions to handle this issue.Moreover,we extend our work to the sliding-window model to meet the requirement of stream evolving issue.Analysis in theorem and experimental reports show that our methods have low space-and time-complexities.

关 键 词：数据流查询 元素对采样 异常频度 滑动窗口 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]