The connection between process complexity of event sequences and models discovered by process mining

Augusto, Adriano; Mendling, Jan ; Vidgof, Maxim und Wurm, Bastian (Juni 2022): The connection between process complexity of event sequences and models discovered by process mining. In: Information Sciences, Bd. 598: S. 196-215

Abstract

Process mining is a research area focusing on the design of algorithms that can auto-matically provide insights into business processes. Among the most popular algorithms are those for automated process discovery, which have the ultimate goal to generate a process model that summarizes the behavior recorded in an event log. Past research had the aim to improve process discovery algorithms irrespective of the characteristics of the input log. In this paper, we take a step back and investigate the connection between measures capturing characteristics of the input event log and the quality of the discov-ered process models. To this end, we review the state-of-the-art process complexity measures, propose a new process complexity measure based on graph entropy, and ana-lyze this set of complexity measures on an extensive collection of event logs and corre-sponding automatically discovered process models. Our analysis shows that many process complexity measures correlate with the quality of the discovered process mod-els, demonstrating the potential of using complexity measures as predictors of process model quality. This finding is important for process mining research, as it highlights that not only algorithms, but also connections between input data and output quality should be studied.

• Dokumententyp: Zeitschriftenartikel
• Keywords: Process complexity; event sequence data; event logs; process mining; graph entropy; automated process discovery
• Fakultät: Betriebswirtschaft > Institut für Digitales Management und Neue Medien ; Betriebswirtschaft > Institut für Digitales Management und Neue Medien > Process and Algorithmic Management
• Themengebiete: 300 Sozialwissenschaften > 330 Wirtschaft
• ISSN: 00200255
• Sprache: Englisch