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Learning of complex event processing rules with genetic programming
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DOI：10.1016/j.eswa.2019.04.007.png)
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Abstract
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Complex Event Processing (CEP) is an established software technology to extract relevant information from massive data streams. Currently, domain experts have to determine manually CEP rules that define a situation of interest. However, often CEP rules cannot be formulated by experts, because the relevant interdependencies and relations between the data are not explicitly known, but inherently hidden in the data streams. To cope with this problem, we present a new learning approach for CEP rules, which is based on Genetic Programming. We discuss in detail the different building blocks of Genetic Programming and how to adjust them to CEP rule learning. Extensive evaluations with synthetic and real world data demonstrate the high potential of the approach and give some hints about the choice of suitable process parameters. (C) 2019 Elsevier Ltd. All rights reserved.
Keywords:
Genetic programming
Complex event processing
Rule learning
Pattern mining
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