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The goal of this special session is to allow experts in the area of process mining to share new techniques, applications and case studies. This session is organized by the IEEE Task Force on Process Mining.

Process mining is a relatively young research discipline that sits between computational intelligence and data mining on the one hand and process modeling and analysis on the other hand. The idea of process mining is to discover, monitor and improve real processes (i.e., not assumed processes) by extracting knowledge from event logs readily available in today's systems.

Process mining provides an important bridge between data mining and business process analysis. Under the Business Intelligence (BI) umbrella many buzzwords have been introduced to refer to rather simple reporting and dashboard tools, such as BAM, CEP, CPM, CPI, BPI, TQM and Six Sigma. These approaches have in common that processes are “put under a microscope” to see whether further improvements are possible. Process mining is an enabling technology for CPM, BPI, TQM, Six Sigma, and the like.

Over the last decade, event data have become readily available and process mining techniques have matured. Process mining algorithms have been implemented in various academic and commercial systems. Today, there is an active group of researchers working on process mining and it has become one of the ”hot topics” in Business Process Management (BPM) research. Moreover, there is a huge interest from industry in process mining. More and more software vendors are adding process mining functionality to their tools.

Considering all these aspects, a special session on process mining can improve the value of the conference by enhancing awareness of typical problems and issues of process mining. Moreover, it is possible to get inspired from classical data mining approaches and methodologies in order to improve analysis of data coming from information systems.

Topics of Interest

Topics of interest include, but are not limited to:

  • Process Mining
  • Business Process Intelligence
  • Automated Business Process Discovery
  • Data Mining for Process Management
  • Conformance checking
  • Specific computational intelligence applications in process mining
  • Case studies


Andrea Burattin, University of Padua, Italy
Fabrizio M. Maggi, University of Technology, The Netherlands