Object-Centric Conformance Alignments with Synchronization (Extended Version)
This work addresses the need for improved object-centric process mining techniques to detect serious problems in logs for domains with complex object dependencies, representing an incremental advancement by hybridizing existing methods.
The paper tackles the problem of accurately reflecting real-world processes with interdependent objects in process mining by introducing a new formalism called 'object-centric Petri nets with identifiers', which combines features from existing approaches to track object identity and dependencies, and proposes a conformance checking approach using satisfiability modulo theories (SMT) encoding, with an evaluation on literature data showing its effectiveness in overcoming earlier limitations.
Real-world processes operate on objects that are inter-dependent. To accurately reflect the nature of such processes, object-centric process mining techniques are needed, notably conformance checking. However, while the object-centric perspective has recently gained traction, few concrete process mining techniques have been presented so far. Moreover, existing approaches are severely limited in their abilities to keep track of object identity and object dependencies. Consequently, serious problems in logs remain undetected. In this paper, we present a new formalism that combines the key modelling features of two existing approaches, in particular the ability of object-centric Petri nets to capture one-to-many relations and the one of Petri nets with identifiers to compare and synchronize objects based on their identity. We call the resulting formalism 'object-centric Petri nets with identifiers', and define alignments and the conformance checking task for this setting. We propose a conformance checking approach for such nets based on an encoding in satisfiability modulo theories (SMT), and illustrate how it can be effectively used to overcome shortcomings of earlier work. To assess its practicality, we perform an evaluation on data from the literature.