Object-Centric Process Mining as the Bridge Between Enterprise Systems and Business Process Management
Wil van der Aalst
Data are collected about anything, at any time, and at any place. Of course, operational processes in production, sales, logistics, finance, and customer services are no exception. These data are stored in enterprise information systems such as SAP, Microsoft Dynamics, Oracle, Kingdee, Yonyou, Salesforce, and Infor. Process mining can be used to remove operational friction by making conformance and performance problems visible. Today, process mining is widely used (especially in Europe) and there are over 35 commercial process mining tools (e.g., Celonis, Disco, UiPath/ProcessGold, myInvenio, PAFnow, Minit, QPR, Mehrwerk, Puzzledata, LanaLabs, StereoLogic, Everflow, TimelinePI, Signavio, and Logpickr). The starting point for process mining are the event data in the systems mentioned before. However, there is a considerable gap between the data stored in enterprise information systems and the event logs needed for process mining. As a result, sometimes 80% of the time is spent on data extraction and only 20% on analysis. Object-centric process mining techniques address this problem by providing an intermediate format for event data and novel process discovery and conformance checking techniques. Traditionally, each event refers to a single case (e.g., an order). This is made possible by flattening event data, but may lead to convergence and divergence problems. The same event may be replicated for multiple cases, or unrelated events may appear to be related. This leads to misleading diagnostics. Moreover, depending on the question at hand, the event data need to be extracted differently. Object-centric process mining helps to address these problems and lowers the time required to extract event data. The keynote introduces object-centric process mining and provides insights that are directly applicable when using existing process mining tools.
Prof.dr.ir. Wil van der Aalst is a full professor at RWTH Aachen University, leading the Process and Data Science (PADS) group. He is also part-time affiliated with the Fraunhofer-Institut für Angewandte Informationstechnik (FIT) where he leads FIT's Process Mining group and the Technische Universiteit Eindhoven (TU/e). Until December 2017, he was the scientific director of the Data Science Center Eindhoven (DSC/e) and led the Architecture of Information Systems group at TU/e. Since 2003, he holds a part-time position at Queensland University of Technology (QUT). Currently, he is also a distinguished fellow of Fondazione Bruno Kessler (FBK) in Trento and a member of the Board of Governors of Tilburg University. His research interests include process mining, Petri nets, business process management, workflow management, process modeling, and process analysis. Wil van der Aalst has published over 230 journal papers, 22 books (as author or editor), 530 refereed conference/workshop publications, and 80 book chapters. Many of his papers are highly cited (he one of the most cited computer scientists in the world; according to Google Scholar, he has an H-index of 150 and has been cited over 100,000 times) and his ideas have influenced researchers, software developers, and standardization committees working on process support. Next to serving on the editorial boards of over ten scientific journals, he is also playing an advisory role for several companies, including Fluxicon, Celonis, and UiPath. Van der Aalst received honorary degrees from the Moscow Higher School of Economics (Prof. h.c.) and Hasselt University (Dr. h.c.). He is also an IFIP Fellow and elected member of the Royal Netherlands Academy of Arts and Sciences, the Royal Holland Society of Sciences and Humanities, the Academy of Europe, and the North Rhine-Westphalian Academy of Sciences, Humanities and the Arts (Nordrhein-Westfälische Akademie der Wissenschaften und der Künste). In 2018, he was awarded an Alexander-von-Humboldt Professorship.