BurstSketch: Finding Bursts in Data Streams
Zheng Zhong, Shen Yan, Zikun Li, Decheng Tan, Tong Yang, Bin Cui
Abstract
1 Burst is a common pattern in data streams which is characterized by a sudden increase in terms of arrival rate followed by a sudden decrease. Burst detection has attracted extensive attention from the research community. In this paper, we propose a novel sketch, namely BurstSketch, to detect bursts accurately in real time. BurstSketch first uses the technique Running Track to select potential burst items efficiently, and then monitors the potential burst items and capture the key features of burst pattern by a technique called Snapshotting. Experimental results show that our sketch achieves a 1.75 times higher recall rate than the strawman solution.
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Install the CLIlune papers fulltext 8d3e10f6-114b-474e-950b-cf4ea22cab19Cited by top-tier papers13
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