Adaptive load shedding for mining frequent patterns from data streams
Data(s) |
01/01/2006
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Resumo |
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.<br /> |
Identificador | |
Idioma(s) |
eng |
Publicador |
Spinger-Verlag |
Relação |
http://dro.deakin.edu.au/eserv/DU:30009021/n20060847.pdf http://dx.doi.org/10.1007/11823728_33 |
Direitos |
2006, Springer-Verlag Berlin Heidelberg |
Palavras-Chave | #mining data streams |
Tipo |
Journal Article |