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AUSDM
2007
Springer

Discovering Frequent Sets from Data Streams with CPU Constraint

9 years 1 months ago
Discovering Frequent Sets from Data Streams with CPU Constraint
Data streams are usually generated in an online fashion characterized by huge volume, rapid unpredictable rates, and fast changing data characteristics. It has been hence recognized that mining over streaming data requires the problem of limited computational resources to be adequately addressed. Since the arrival rate of data streams can significantly increase and exceed the CPU capacity, the machinery must adapt to this change to guarantee the timeliness of the results. We present an online algorithm to approximate a set of frequent patterns from a sliding window over the underlying data stream – given apriori CPU capacity. The algorithm automatically detects overload situations and can adaptively shed unprocessed data to guarantee the timely results. We theoretically prove, using probabilistic and deterministic techniques, that the error on the output results is bounded within a pre-specified threshold. The empirical results on various datasets also confirmed the feasiblity of...
Xuan Hong Dang, Wee Keong Ng, Kok-Leong Ong, Vince
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where AUSDM
Authors Xuan Hong Dang, Wee Keong Ng, Kok-Leong Ong, Vincent C. S. Lee
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