Discovering System Dynamics Simulation Models Using Process Mining

• Published in: IEEE access Vol. 10; p. 1
• Main Authors: Pourbafrani, Mahsa, Van Der Aalst, Wil M.P.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical models; coarse-grained process logs; Data mining; Data models; Discrete event systems; event logs; Information systems; Machine learning; Mathematical models; Predictive models; Process mining; Process variables; scenario-based predictions; Simulation; Simulation models; System dynamics; what-if analysis
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3193507

Process mining techniques are able to describe and model real processes using historic event data extracted from the information systems of organizations. Later, these insights are used for process improvement. For instance, Discrete Event Simulation (DES) uses process models that are able to mimic real-world events. However, the aggregated performance status of processes over time reveals various hidden relationships between process variables. Coarse-grained process logs are sets of performance variables over steps of time, generated using event data from processes. The coarse-grained process logs describe processes at higher levels. System Dynamics completes process mining by capturing the relationships between various process variables at a higher level of abstraction. In this paper, we propose a new framework for capturing conceptual models of processes using transformed event data. The main idea is to automatically discover the underlying relations as equations. This allows us to generate system dynamics simulations of processes. We employ a variety of statistical and machine learning techniques to discover the hidden relationships between process variables. The framework supports the simulation modeling task in the context of system dynamics simulations. The experiments using real event logs demonstrate that our approach is able to generate valid models and capture the underlying relationships.