Automatic Learning of Predictive CEP Rules: Bridging the Gap between Data Mining and Complex Event Processing

Abstract : Due to the undeniable advantage of prediction and proactivity, many research areas and industrial applications are accelerating the pace to keep up with data science and predictive analytics. However and due to three well-known facts, the reactive Complex Event Processing (CEP) technology might lag behind when prediction becomes a requirement. 1st fact: The one and only inference mechanism in this domain is totally guided by CEP rules. 2nd fact: The only way to define a CEP rule is by writing it manually with the help of a human expert. 3rd fact: Experts tend to write reactive CEP rules, because and regardless of the level of expertise, it is nearly impossible to manually write predictive CEP rules. Combining these facts together, the CEP is---and will stay--- a reactive computing technique. Therefore in this article, we present a novel data mining-based approach that automatically learns predictive CEP rules. The approach proposes a new learning algorithm where complex patterns from multivariate time series are learned. Then at run-time, a seamless transformation into the CEP world takes place. The result is a ready-to-use CEP engine with enrolled predictive CEP rules. Many experiments on publicly-available data sets demonstrate the effectiveness of our approach.
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Communication dans un congrès
DEBS '17 Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems , 2017, Barcelone, Spain. 〈10.1145/3093742.3093917〉
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https://hal.archives-ouvertes.fr/hal-01558267
Contributeur : Raef Mousheimish <>
Soumis le : vendredi 7 juillet 2017 - 13:33:13
Dernière modification le : jeudi 21 septembre 2017 - 01:05:35

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Raef Mousheimish, Yehia Taher, Karine Zeitouni. Automatic Learning of Predictive CEP Rules: Bridging the Gap between Data Mining and Complex Event Processing. DEBS '17 Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems , 2017, Barcelone, Spain. 〈10.1145/3093742.3093917〉. 〈hal-01558267〉

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