IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v53y2015i13p3888-3916.html
   My bibliography  Save this article

An Integrated mining approach to discover business process models with parallel structures: towards fitness improvement

Author

Listed:
  • Chao Ou-Yang
  • Hsin-Jung Cheng
  • Yeh-Chun Juan

Abstract

Process mining (PM) is a technique to extract a process model from an event log to represent the process behaviour recorded in that event log. A mined process model with high fitness means that it can reflect most of the process behaviour recorded in the event log. Previous studies have shown that the mined model with high fitness can be used in process improvement, such as fraud detection, continuous process improvement and benchmarking. Genetic process mining (GPM) is a famous PM approach, which can simultaneously identify several process structures from event logs. However, GPM cannot effectively discover parallel structures from event logs. This study proposes a PM approach based on integration of GPM, particle swarm optimisation and differential evolution to find process models with high fitness for event logs involving multiple parallel structures. The results show that the proposed approach does indeed lead to improvement in gaining process models with high fitness for event logs involving multiple parallel structures.

Suggested Citation

  • Chao Ou-Yang & Hsin-Jung Cheng & Yeh-Chun Juan, 2015. "An Integrated mining approach to discover business process models with parallel structures: towards fitness improvement," International Journal of Production Research, Taylor & Francis Journals, vol. 53(13), pages 3888-3916, July.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:13:p:3888-3916
    DOI: 10.1080/00207543.2014.974847
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2014.974847
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2014.974847?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:tprsxx:v:53:y:2015:i:13:p:3888-3916. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.