Task Force on Process Mining

The IEEE Task Force on Process Mining (TFPM) is an non-commercial association for process mining. The IEEE Task Force on Process Mining was established in October 2009 as part of the IEEE Computational Intelligence Society.[1] The task force is supported by over 80 organizations and has around 750 members.[2] The main goal of the task force is to promote the research, development, education, and understanding of process mining.[3]

Goals

The goals of the Task Force on Process Mining include:

  • promote the research, development, education and understanding of process mining,
  • make end-users, developers, consultants, and researchers aware of the state-of-the-art in process mining,
  • promote the use of process mining techniques and tools and stimulate new applications,
  • play a role in standardization efforts for logging event data (e.g., XES),
  • organize tutorials, special sessions, workshops, competitions, panels, and
  • develop material (papers, books, online courses, movies, etc.) to inform and guide people new to the field.

Activities and Organization

The Task Force on Process Mining has a Steering Committee[4] and an Advisory Board.[5] The Steering Committee, chaired by Wil van der Aalst since its inception in 2009, defined 15 action lines. These include the organization of the annual International Process Mining Conference (ICPM) series,[6] standardization efforts leading to the IEEE XES standard for storing and exchanging event data[7][8], and the Process Mining Manifesto[9] which was translated into 16 languages. The Task Force on Process Mining also publishes a newsletter, provides data sets, organizes workshops and competitions, and connects researchers and practitioners.

Supporting Organizations

The Task Force on Process Mining is supported by most of the process mining vendors (e.g., Celonis, Fluxicon, UiPath, QPR, ABBYY, LANA, Logpicker, Minit, Myinvenio, PAFnow, Signavio and Software AG), consultancy firms (KPMG, Deloitte, etc.), universities (e.g., RWTH, TU/e, QUT, UniBZ, and DTU), research institutes (e.g., Fraunhofer FIT), and organizations using process mining at a large scale (e.g., ABB, Bosch, and Siemens).[10] In total over 80 organizations support the task force and there are around 750 individual members.

See also

References
  1. "IEEE Task Force on Process Mining". Home page of the task force on process mining. IEEE Task Force on Process Mining. Retrieved 10 January 2021.
  2. "Supporting organizations - IEEE Task Force on Process Mining". www.tf-pm.org. Retrieved 2021-01-10.
  3. van der Aalst, Wil (2016). Process Mining: Data Science in Action.
  4. "Steering Committee - IEEE Task Force on Process Mining". www.tf-pm.org. Retrieved 2021-01-10.
  5. "Advisory Board - IEEE Task Force on Process Mining". www.tf-pm.org. Retrieved 2021-01-10.
  6. "International Process Mining Conference (ICPM) series". Home page of the ICPM conference series. IEEE Task Force on Process Mining. Retrieved 10 January 2021.
  7. "IEEE Standard for eXtensible Event Stream (XES) for Achieving Interoperability in Event Logs and Event Streams". IEEE Standard for eXtensible Event Stream (XES). ieee. 11 November 2016. Retrieved 10 January 2021.
  8. "eXtensible Event Stream (XES)". eXtensible Event Stream (XES). IEEE Task Force on Process Mining. 11 November 2016. Retrieved 10 January 2021.
  9. "Process Mining Manifesto". Process Mining Manifesto. IEEE Task Force on Process Mining. 2011. Retrieved 10 January 2021.
  10. "Supporting organizations - IEEE Task Force on Process Mining". www.tf-pm.org. Retrieved 2021-01-10.

Further reading
  • Aalst, W. van der (2016). Process Mining: Data Science in Action. Springer Verlag, Berlin (ISBN 978-3-662-49850-7).
  • Reinkemeyer, L. (2020). Process Mining in Action: Principles, Use Cases and Outlook. Springer Verlag, Berlin (ISBN 978-3-030-40171-9).
  • IEEE Task Force on Process Mining. Process Mining Manifesto. In F. Daniel, K. Barkaoui, and S. Dustdar, editors, Business Process Management Workshops, volume 99 of Lecture Notes in Business Information Processing, pages 169–194. Springer-Verlag, Berlin, 2012 (open access). https://link.springer.com/chapter/10.1007%2F978-3-642-28108-2_19
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