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

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

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

Content Provider	ACM Digital Library
Author	Taher, Yehia Zeitouni, Karine Mousheimish, Raef
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.
Starting Page	158
Ending Page	169
Page Count	12
File Format	PDF
ISBN	9781450350655
DOI	10.1145/3093742.3093917
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-06-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Rule discovery Multivariate time series Early classification Complex event processing Time series data mining
Content Type	Text
Resource Type	Article

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