Applying Sequence Mining for Outlier Detection in Process Mining View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-10-18

AUTHORS

Mohammadreza Fani Sani , Sebastiaan J. van Zelst , Wil M. P. van der Aalst

ABSTRACT

One of the challenges in applying process mining algorithms on real event data, is the presence of outlier behavior. Such behaviour often leads to complex, incomprehensible, and, sometimes, even inaccurate process mining results. As a result, correct and/or important behaviour of the process may be concealed. In this paper, we exploit sequence mining techniques for the purpose of outlier detection in the process mining domain. Using the proposed approach, it is even possible to detect outliers in case of heavy parallelism and/or long-term dependencies between business process activities. Our method has been implemented in both the ProM- and the RapidProM framework. Using these implementations, we conducted a collection of experiments that show that we are able to detect and remove outlier behaviour in event data. Our evaluation clearly demonstrates that the proposed method accurately removes outlier behaviour and, indeed, improves process discovery results. More... »

PAGES

98-116

References to SciGraph publications

  • 2019-02. Discovering more precise process models from event logs by filtering out chaotic activities in JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
  • 2018-05-17. Filtering Spurious Events from Event Streams of Business Processes in ADVANCED INFORMATION SYSTEMS ENGINEERING
  • 2006-07. A Rule-Based Approach for Process Discovery: Dealing with Noise and Imbalance in Process Logs in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2014. Discovering Block-Structured Process Models from Event Logs Containing Infrequent Behaviour in BUSINESS PROCESS MANAGEMENT WORKSHOPS
  • 2007-10. Mining process models with non-free-choice constructs in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2018-06-16. Repairing Outlier Behaviour in Event Logs in BUSINESS INFORMATION SYSTEMS
  • 2018. Improving Process Discovery Results by Filtering Outliers Using Conditional Behavioural Probabilities in BUSINESS PROCESS MANAGEMENT WORKSHOPS
  • 2016-11. Scientific workflows for process mining: building blocks, scenarios, and implementation in INTERNATIONAL JOURNAL ON SOFTWARE TOOLS FOR TECHNOLOGY TRANSFER
  • 2012. On the Role of Fitness, Precision, Generalization and Simplicity in Process Discovery in ON THE MOVE TO MEANINGFUL INTERNET SYSTEMS: OTM 2012
  • 2007. Fuzzy Mining – Adaptive Process Simplification Based on Multi-perspective Metrics in BUSINESS PROCESS MANAGEMENT
  • 2015. Avoiding Over-Fitting in ILP-Based Process Discovery in BUSINESS PROCESS MANAGEMENT
  • 2013. Discovering Block-Structured Process Models from Event Logs - A Constructive Approach in APPLICATION AND THEORY OF PETRI NETS AND CONCURRENCY
  • Book

    TITLE

    On the Move to Meaningful Internet Systems. OTM 2018 Conferences

    ISBN

    978-3-030-02670-7
    978-3-030-02671-4

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-030-02671-4_6

    DOI

    http://dx.doi.org/10.1007/978-3-030-02671-4_6

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1107688575


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