Discovering System Dynamics Simulation Models Using Process Mining

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.

Automated summary

Process mining techniques use historic event data to describe and model real processes, which can be used for process improvement. Coarse-grained process logs describe performance variables over time, while System Dynamics captures relationships between process variables at a higher level of abstraction. This paper proposes a new framework that automatically discovers underlying relationships and generates System Dynamics simulations using transformed event data.