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Optimizing event pattern matching using business process models

  • A growing number of enterprises use complex event processing for monitoring and controlling their operations, while business process models are used to document working procedures. In this work, we propose a comprehensive method for complex event processing optimization using business process models. Our proposed method is based on the extraction of behaviorial constraints that are used, in turn, to rewrite patterns for event detection, and select and transform execution plans. We offer a set of rewriting rules that is shown to be complete with respect to the all, seq, and any patterns. The effectiveness of our method is demonstrated in an experimental evaluation with a large number of processes from an insurance company. We illustrate that the proposed optimization leads to significant savings in query processing. By integrating the optimization in state-of-the-art systems for event pattern matching, we demonstrate that these savings materialize in different technical infrastructures and can be combined with existing optimizationA growing number of enterprises use complex event processing for monitoring and controlling their operations, while business process models are used to document working procedures. In this work, we propose a comprehensive method for complex event processing optimization using business process models. Our proposed method is based on the extraction of behaviorial constraints that are used, in turn, to rewrite patterns for event detection, and select and transform execution plans. We offer a set of rewriting rules that is shown to be complete with respect to the all, seq, and any patterns. The effectiveness of our method is demonstrated in an experimental evaluation with a large number of processes from an insurance company. We illustrate that the proposed optimization leads to significant savings in query processing. By integrating the optimization in state-of-the-art systems for event pattern matching, we demonstrate that these savings materialize in different technical infrastructures and can be combined with existing optimization techniques.show moreshow less

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Metadaten
Author details:Matthias Weidlich, Holger Ziekow, Avigdor Gal, Jan Mendling, Mathias WeskeORCiDGND
DOI:https://doi.org/10.1109/TKDE.2014.2302306
ISSN:1041-4347
ISSN:1558-2191
Title of parent work (English):IEEE transactions on knowledge and data engineering
Publisher:Inst. of Electr. and Electronics Engineers
Place of publishing:Los Alamitos
Publication type:Article
Language:English
Year of first publication:2014
Publication year:2014
Release date:2017/03/27
Tag:Event processing; query optimisation; query rewriting
Volume:26
Issue:11
Number of pages:15
First page:2759
Last Page:2773
Funding institution:FP7 grant [318275, 318225]
Organizational units:An-Institute / Hasso-Plattner-Institut für Digital Engineering gGmbH
Peer review:Referiert
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