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

We now live in a time where the amount of data created daily goes easily beyond the storage and processing capabilities of nowadays systems: organizations, governments but also individuals generate large amounts of data at a rate that has started to overwhelm the ability to timely extract useful knowledge from it. Nevertheless the strategic importance of the knowledge hidden in such data, for effective decision making is paramount and need to be extracted quickly in order to effectively react to dynamic situations.  Efficient stream processing approaches for real time analysis are crucial for enabling the predictive capabilities required by today's dynamically and rapidly evolving enterprises.  Moreover, since the work of medium-large enterprises is typically governed by business processes, it is very common to have event data generated as result of such process executions.

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.

Finally the level of maturity and the relative low-cost of distributed approaches for storage and processing of information has not been fully exploited by the process mining community. There are very few research results on distributed storage methods and process mining algorithms.

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. In addition, 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:

  • Storage and extraction of big process logs
  • Process mining approaches
  • Online process mining (stream processing)
  • Distributed approaches for process mining
  • Business process intelligence
  • Data mining for process management
  • Specific computational intelligence applications in process mining
  • Case studies


Andrea Burattin, University of Padua, Italy
Fabrizio M. Maggi, University of Tartu, Estonia
Marcello Leida, Etisalat BT Innovation Centre, United Arab Emirates