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	Step-by-step regression: A more efficient alternative for polynomial multiple linear regression in stream cube
	Liu, C ; Zhang, M ; Zheng, MR ; Chen, YX
	2003
英文摘要	Facing tremendous and potentially infinite stream data, it is impossible to record them entirely. Thus synopses are required to be generated timely to capture the underlying model for stream management systems. Traditionally, curve fitting through Multiple Linear Regression (MLR) is a powerful and efficient modeling tool. In order to further accelerate its processing efficiency, we propose Step-by-step Regression (SR) as a more efficient alternative. As revealed in experiments, it speeds up for more than 40 times. In addition, inspired by previous work, we integrated SR into cube environment through similar compression technique to perform online analytical processing and mining over data stream. Finally, experiments show that SR not only significantly alleviates the computation pressure on the front ends of data stream management systems, but also results in a much smaller stream cube for on line analysis and real-time surveillance.; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000184716000044&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Software Engineering; SCI(E); CPCI-S(ISTP); 2
语种	英语
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/292343]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Liu, C,Zhang, M,Zheng, MR,et al. Step-by-step regression: A more efficient alternative for polynomial multiple linear regression in stream cube. 2003-01-01.

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