IEEE CIS Task Force on Process Mining

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Due to the success of the ICPM conference series and the growing importance of process mining, the IEEE Task Force on Process Mining is in the process of being restructured. A new Steering Committee has been formed, but it will take some time before the anticipated changes have been be implemented. Many new activities have been planned and the membership structure will be evaluated and changed. Therefore, we add the disclaimer that things will change again in the coming year.

More and more people, both in industry and academia, consider process mining (see the promotional video for an introduction) as one of the most important innovations in the field of business process management. It joins ideas of process modeling and analysis on the one hand and data mining and machine learning on the other. Therefore, the IEEE has established a Task Force on Process Mining. This Task Force is established in the context of the Data Mining Technical Committee (DMTC) of the Computational Intelligence Society (CIS) of the Institute of Electrical and Electronic Engineers, Inc. (IEEE).

The goal of this Task Force is to promote the research, development, education and understanding of process mining. More concretely, the goal is to:

  • 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 stimulating new applications,
  • play a role in standardization efforts for logging event data,
  • the organization of tutorials, special sessions, workshops, panels,
  • the organization of Conferences/Workshop with IEEE CIS Technical Co-Sponsorship, and
  • publications in the form of special issues in journals, books, articles (e.g., in the IEEE Computational Intelligence Magazine).

Note that process mining includes (automated) process discovery (extracting process models from an event log), conformance checking (monitoring deviations by comparing model and log), social network/organizational mining, automated construction of simulation models, case prediction, and history-based recommendations.

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