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DASFAA
2005
IEEE

Adaptively Detecting Aggregation Bursts in Data Streams

9 years 5 months ago
Adaptively Detecting Aggregation Bursts in Data Streams
Finding bursts in data streams is attracting much attention in research community due to its broad applications. Existing burst detection methods suffer the problems that 1) the parameters of window size and absolute burst threshold, which are hard to be determined a priori, should be given in advance. 2) Only one side bursts, i.e. either increasing or decreasing bursts, can be detected. 3) Bumps, which are changes of aggregation data caused by noises, are often reported as bursts. The disturbance of bumps causes much effort in subsequent exploration of mining results. In this paper, a general burst model is introduced for overcoming above three problems. We develop an efficient algorithm for detecting adaptive aggregation bursts in a data stream given a burst ratio. With the help of a novel inverted histogram, the statistical summary is compressed to be fit in limited main memory, so that bursts on windows of any length can be detected accurately and efficiently on-line. Theoretica...
Aoying Zhou, Shouke Qin, Weining Qian
Added 24 Jun 2010
Updated 24 Jun 2010
Type Conference
Year 2005
Where DASFAA
Authors Aoying Zhou, Shouke Qin, Weining Qian
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