Meeting IEEE Task Force on Process Mining at BPM 2010
Calder Room, Howe Center, 3rd Floor
Stevens Institute of Technology
Wednesday, September 15th 2010, 15.30-17.00
- Introduction of participants and Task Force
- Goals of the Task Force
- Discussion of activities to promote the topic of process mining
- Standardization efforts: Discussion of XES format (see also http://www.xes-standard.org/)
- Open discussion of various topics, e.g., “tool support for process mining”, “lecture material”, “earning money with process mining”, “role of consulting”, “embedding of process mining technology of other tools”.
More information on the IEEE Task Force on Process Mining:
(Also see http://www.win.tue.nl/ieeetfpm/)
The Task Force was established in 2009 stimulate process mining research and development activities and to create a better awareness of process mining in industry.
More and more people, both in industry and academia, consider process mining 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.