%0 Journal Article
%T Fractal-Based Algorithms for Burst Detection over Data Streams<br>基于分形技术的数据流突变检测算法
%A QIN Shou-Ke
%A QIAN Wei-Ning
%A ZHOU Ao-Ying
%A <br>秦首科
%A 钱卫宁
%A 周傲英
%J 软件学报
%D 2006
%I 
%X Burst detection over data streams has been attracting more and more attention from academic and industry communities due to its broad potential applications in venture analysis, network monitoring, trend analysis and so on. This paper aims at detecting bursts of both monotonic and non-monotonic aggregates over multiple windows in data streams. A burst detection algorithm through building monotonic search space based on fractal technique is proposed. First, the piecewise fractal model on data stream is introduced, and then based on this model the algorithm for detecting bursts is presented. The proposed algorithm can decrease the time complexity from O(m) to O(logm), where m is the number of sliding windows being detected. Two novel piecewise fractal models can model the self-similarity and compress data streams with high accuracy. Theoretical analysis and experimental results show that this algorithm can achieve a higher precision with less space and time complexity as compared with the existing methods, and it could be concluded that the proposed algorithm is suitable for burst detection over data streams.
%K data stream
%K burst detection
%K fractal
%K piecewise fractal model
%K search space<br>数据流
%K 突变检测
%K 分形
%K 分段分形模型
%K 搜索空间
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=7735F413D429542E610B3D6AC0D5EC59&aid=FDD689585C6DC47B&yid=37904DC365DD7266&vid=BCA2697F357F2001&iid=9CF7A0430CBB2DFD&sid=F3D9B969D1F8BD1F&eid=8C8371356FB4C85C&journal_id=1000-9825&journal_name=软件学报&referenced_num=4&reference_num=16