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Rediscovering workflow models from event-based data using little thumb

Published: 01 April 2003 Publication History

Abstract

Contemporary workflow management systems are driven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and typically, there are discrepancies between the actual workflow processes and the processes as perceived by the management. Therefore, we propose a technique for rediscovering workflow models. This technique uses workflow logs to discover the workflow process as it is actually being executed. The workflow log contains information about events taking place. We assume that these events are totally ordered and each event refers to one task being executed for a single case. This information can easily be extracted from transactional information systems (e.g., Enterprise Resource Planning systems such as SAP and Baan). The rediscovering technique proposed in this paper can deal with noise and can also be used to validate workflow processes by uncovering and measuring the discrepancies between prescriptive models and actual process executions.

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  • (2023)Timed partial order inference algorithmProceedings of the Thirty-Third International Conference on Automated Planning and Scheduling10.1609/icaps.v33i1.27246(639-647)Online publication date: 8-Jul-2023
  • (2019)A Novel Process Mining Algorithm to Discover Non-free Choice Construct from Event LogsProceedings of the 3rd International Conference on Computer Science and Application Engineering10.1145/3331453.3360956(1-6)Online publication date: 22-Oct-2019
  • (2019)Comparing Concept Drift Detection with Process Mining ToolsProceedings of the XV Brazilian Symposium on Information Systems10.1145/3330204.3330240(1-8)Online publication date: 20-May-2019
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      Published In

      cover image Integrated Computer-Aided Engineering
      Integrated Computer-Aided Engineering  Volume 10, Issue 2
      April 2003
      96 pages

      Publisher

      IOS Press

      Netherlands

      Publication History

      Published: 01 April 2003

      Author Tags

      1. knowledge discovery
      2. petri nets
      3. process mining
      4. workflow mining

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      • (2023)Timed partial order inference algorithmProceedings of the Thirty-Third International Conference on Automated Planning and Scheduling10.1609/icaps.v33i1.27246(639-647)Online publication date: 8-Jul-2023
      • (2019)A Novel Process Mining Algorithm to Discover Non-free Choice Construct from Event LogsProceedings of the 3rd International Conference on Computer Science and Application Engineering10.1145/3331453.3360956(1-6)Online publication date: 22-Oct-2019
      • (2019)Comparing Concept Drift Detection with Process Mining ToolsProceedings of the XV Brazilian Symposium on Information Systems10.1145/3330204.3330240(1-8)Online publication date: 20-May-2019
      • (2018)Discovering Process Horizontal Boundaries to Facilitate Process ComprehensionInternational Journal of Operations Research and Information Systems10.4018/IJORIS.20180401019:2(1-31)Online publication date: 1-Apr-2018
      • (2018)Process variant comparisonInformation Systems10.1016/j.is.2017.12.00674:P1(53-66)Online publication date: 1-May-2018
      • (2018)Analyzing and improving multi-robot missions by using process miningAutonomous Robots10.1007/s10514-017-9686-142:6(1187-1205)Online publication date: 1-Aug-2018
      • (2018)Event stream-based process discovery using abstract representationsKnowledge and Information Systems10.1007/s10115-017-1060-254:2(407-435)Online publication date: 1-Feb-2018
      • (2018)Discovering workflow nets using integer linear programmingComputing10.1007/s00607-017-0582-5100:5(529-556)Online publication date: 1-May-2018
      • (2017)An Insider Threat Detection Method Based on Business Process MiningInternational Journal of Business Data Communications and Networking10.4018/ijbdcn.201707010713:2(83-98)Online publication date: 1-Jul-2017
      • (2017)Cognitive ComputingIEEE Intelligent Systems10.1109/MIS.2017.312155432:4(3-4)Online publication date: 1-Jan-2017
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