Adaptive load shedding for mining frequent patterns from data streams

Dang, X., Ng, W. and Ong, Kok-Leong 2006, Adaptive load shedding for mining frequent patterns from data streams, Lecture notes in computer science, vol. 4081, pp. 342-351, doi: 10.1007/11823728_33.

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Title Adaptive load shedding for mining frequent patterns from data streams
Author(s) Dang, X.
Ng, W.
Ong, Kok-Leong
Journal name Lecture notes in computer science
Volume number 4081
Start page 342
End page 351
Publisher Spinger-Verlag
Place of publication Berlin, Germany
Publication date 2006
ISSN 0302-9743
Keyword(s) mining data streams
Summary Most algorithms that focus on discovering frequent patterns from data streams assumed that the machinery is capable of managing all the incoming transactions without any delay; or without the need to drop transactions. However, this assumption is often impractical due to the inherent characteristics of data stream environments. Especially under high load conditions, there is often a shortage of system resources to process the incoming transactions. This causes unwanted latencies that in turn, affects the applicability of the data mining models produced – which often has a small window of opportunity. We propose a load shedding algorithm to address this issue. The algorithm adaptively detects overload situations and drops transactions from data streams using a probabilistic model. We tested our algorithm on both synthetic and real-life datasets to verify the feasibility of our algorithm.
Notes Book title : "Data Warehousing and Knowledge Discovery"
Language eng
DOI 10.1007/11823728_33
Field of Research 080605 Decision Support and Group Support Systems
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2006, Springer-Verlag Berlin Heidelberg
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