Multi-Perspective Declarative Process Mining
Process discovery is one of the main branches of process mining that allows the user to build a process model representing the process behavior as recorded in the logs. Standard process discovery techniques produce as output a procedural process model (e.g., a Petri net). Recently, several approaches have been developed to derive declarative process models from logs and have been proven to be more suitable to analyze processes working in environments that are less stable and predictable. However, a large part of these techniques are focused on the analysis of the control flow perspective of a business process. Therefore, one of the challenges still open in this field is the development of techniques for the analysis of business processes also from other perspectives, like data, time, and resources. In this research talk, an approach for the discovery of multi-perspective declarative process models from event logs is presented that allows the user to discover declarative models taking into consideration all the information an event log can provide.
Stefan Schönig is an assistant professor at the Process Management group at University of Bayreuth in Germany. He graduated as a B.Sc. (2009) and as an M.Sc. (2011) in Applied Computer Science at University of Bayreuth in Germany. He received his Ph.D. in 2015 at University of Bayreuth. From 2015 to 2016, he worked as post-doctoral researcher with the Institute for Information Business atVienna University of Economics and Business (Austria). His research is focused on the analysis of executed business processes. He has participated in several industry projects that addressed process mining, process analysis, process monitoring and process intelligence. Based on his work, he has published several scientific papers in international conferences such as CAiSE and ICSOC and journals, including Decision Support Systems and Software and System Modeling.