Discovering System Dynamics Simulation Models Using Process Mining

IEEE Access, vol. 10, pp. 78527–78547

Abstract

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 Dynamicscompletes 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.

Authors 2

1. Mahsa Pourbafrani Aachen Process and Data Science Group

   RWTH Aachen University

   Affiliation as printed

   Process and Data Science Group, RWTH Aachen University, Aachen, Germany

2. Wil M. P. van der Aalst

   Fraunhofer Institute for Applied Information Technology

   Affiliation as printed

   FIT GmbH, Sankt Augustin, Germany