In recent years, process mining, a promising technique for optimizing business processes, gained a lot of attention from both industry and research. The most process mining approaches focus on the activity-centric business process management paradigm. In contrast, due to the increasing importance of data in business processes, data-centric business process management emerged. Whereas the widely used activity-centric paradigm sets the focus on the coordinated execution of several business functions (i.e., activities) in processes, the data-centric paradigm focuses on the availability of data in processes. Within the data-centric paradigm several approaches have been developed. One is the object-aware approach, which is engaged in the connection of business objects (i.e., objects involved in the process, e.g., a product in a manufacturing process) and business processes. A framework for object-aware processes is PHILharmonicFlows. There only exist few approaches to enable process mining in data-centric processes, this applies in particular to object-aware processes. For this reason, this thesis aims at the enabling of one type of process mining, conformance checking, in object-aware business processes. The approach of this thesis is the following: The object-aware process models are translated into petri nets, because this is necessary to enable existing conformance checking algorithms. In detail, the procedure is to identify process patterns in the process models and to map them onto petri nets. These process patterns are then combined to model an exemplary object-aware process as a whole. The generic description of the identified patterns permits to apply them to any object-aware process.
BA Abschlussvortrag, Christopher Hirschenberger, Ort: Online, Datum: 12.05.2021, Zeit: 10:45