Reasoning on responsibilities for optimal process alignment computation

• Published in: Data & knowledge engineering Vol. 154; p. 102353
• Main Authors: Baldoni, Matteo, Baroglio, Cristina, Marengo, Elisa, Micalizio, Roberto
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2024
• Subjects: Formal methods; Knowledge representation and reasoning; Process mining; Process mining; Formal methods; Knowledge representation and reasoning
• ISSN: 0169-023X
• DOI: 10.1016/j.datak.2024.102353

Process alignment aims at establishing a matching between a process model run and a log trace. To improve such a matching, process alignment techniques often exploit contextual conditions to enable computations that are more informed than the simple edit distance between model runs and log traces. The paper introduces a novel approach to process alignment which relies on contextual information expressed as responsibilities. The notion of responsibility is fundamental in business and organization models, but it is often overlooked. We show the computation of optimal alignments can take advantage of responsibilities. We leverage on them in two ways. First, responsibilities may sometimes justify deviations. In these cases, we consider them as correct behaviors rather than errors. Second, responsibilities can either be met or neglected in the execution of a trace. Thus, we prefer alignments where neglected responsibilities are minimized. The paper proposes a formal framework for responsibilities in a process model, including the definition of cost functions for computing optimal alignments. We also propose a branch-and-bound algorithm for optimal alignment computation and exemplify its usage by way of two event logs from real executions.