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Local Concurrency Detection in Business Process Event Logs

Published:21 January 2019Publication History
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Abstract

Process mining techniques aim at analyzing records generated during the execution of a business process in order to provide insights on the actual performance of the process. Detecting concurrency relations between events is a fundamental primitive underpinning a range of process mining techniques. Existing approaches to this problem identify concurrency relations at the level of event types under a global interpretation. If two event types are declared to be concurrent, every occurrence of one event type is deemed to be concurrent to one occurrence of the other. In practice, this interpretation is too coarse-grained and leads to over-generalization. This article proposes a finer-grained approach, whereby two event types may be deemed to be in a concurrency relation relative to one state of the process, but not relative to other states. In other words, the detected concurrency relation holds locally, relative to a set of states. Experimental results both with artificial and real-life logs show that the proposed local concurrency detection approach improves the accuracy of existing concurrency detection techniques.

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      • Published in

        cover image ACM Transactions on Internet Technology
        ACM Transactions on Internet Technology  Volume 19, Issue 1
        Regular Papers, Special Issue on Service Management for IOT and Special Issue on Knowledge-Driven BPM
        February 2019
        321 pages
        ISSN:1533-5399
        EISSN:1557-6051
        DOI:10.1145/3283809
        • Editor:
        • Ling Liu
        Issue’s Table of Contents

        Copyright © 2019 ACM

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 21 January 2019
        • Accepted: 1 October 2018
        • Revised: 1 June 2018
        • Received: 1 December 2016
        Published in toit Volume 19, Issue 1

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