Automatic Discrete Event Simulation in Process Mining



Mahsa Pourbafrani

Wissenschaftliche Mitarbeiterin


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BSc Thesis Project

Title: Automatic Discrete Event Simulation in Process Mining

Author: Shuai Jiao

Supervisor: Mahsa Pourbafrani

1st examiner: Prof.Dr. ir. Wil M.P. van der Aalst

2nd examiner: Prof. Dr. Stefan Decker


Process mining is a bridge between data science and process science. When conducting quantitative analysis of processes, it is a general approach to use simulation. Among various techniques for simulation, discrete event simulation (DES) is the most capable and powerful one. Therefore, this research combines both process mining and simulation techniques to construct an automatic discrete event simulation system. Since the process tree is a sound process model [3], our research is in terms of the process tree that can be generated by the inductive miner approach. To mimic the real-life process, the process routing has to be controlled while exploring processes from the process tree in our approach. This thesis contributes a Python-based web-applicable tool SIMPT (Interactive Simulation of Time-aware Process Trees) based on PM4Py and SimPy, which integrates multiple perspectives, such as process discovery, process tree analysis, organizational analysis, performance analysis, and simulation. The results of this research are expected to be applied in operational decision and business process management.