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Construction of Synopsis for Periodically Updating Sliding Windows over Data Streams

机译:定期更新数据流的Sypidsis的概要构建

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In many applications including network monitoring, web click stream analysis, sensor networks, detection of network intrusions, telecommunications data management and financial applications, data arrives in a stream fashion. The main focus in algorithms for data streams has been on efficient construction of synopsis data structures. This paper introduces the problem of construction of synopsis data structures from periodically updating sliding windows over data streams, and presents a new sampling-based synopsis data structure and new techniques for its fast incremental maintenance. We use the basic window technique in conjunction with reservoir sampling algorithm to present a novel algorithm, which is called RSAP Algorithm. The algorithm is not an unbiased one but a stratified unbiased random sampling algorithm. The experiments show that the new algorithm is effective and efficient for construction of summary structures from sliding windows over data streams.
机译:在包括网络监控的许多应用中,Web单击流分析,传感器网络,网络入侵检测,电信数据管理和金融应用,数据以流方式到达。数据流算法中的主要焦点是有效地构建概要数据结构。本文介绍了从周期性地更新数据流的Sypopsis数据结构的构建问题,并提出了一种新的基于采样的概要数据结构和新技术,用于其快速增量维护。我们使用基本窗口技术与储库采样算法结合呈现一种新颖的算法,称为RSAP算法。该算法不是一个非偏见的一个,而是一个分层的非偏见随机采样算法。实验表明,新算法对于将窗口滑动窗口的摘要结构构建是有效且有效的。

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