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Data-driven simulation in process mining: introducing a reference model

• Mahsa Pourbafrani
• Wil M.P. van der Aalst

(2023). ECMS 2023, 37th Proceedings
Edited by: Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni, European Council for Modelling and Simulation.
DOI: http://doi.org/10.7148/2023
ISSN: 2522-2422 (ONLINE)
ISSN: 2522-2414 (PRINT)
ISSN: 2522-2430 (CD-ROM)
ISBN: 978-3-937436-80-7
ISBN: 978-3-937436-79-1 (CD) Communications of the ECMS Volume 37, Issue 1, June 2023, Florence, Italy June 20th – June 23rd, 2023

https://doi.org/10.7148/2023-0411

Mahsa pourbafrani, Wil m.p. van der aalst (2023). Data-driven Simulation in Process Mining: Introducing a Reference Model, ECMS 2023, Proceedings Edited by: Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni, European Council for Modelling and Simulation. doi:10.7148/2023-0411

Different approaches are proposed for simulating processes in process mining. There are open challenges while designing the simulation models of processes: (1) the quality of the designed models is mostly evaluated using simulation results, and the models themselves do not get validated, (2) the choice of process aspects to be considered in the simulation models of processes as simulation parameters is rather arbitrary, e.g., considering multitasking, and (3) the distinction between the acquiring simulation parameters step and the parameters' regeneration step is not defined. This paper aims to introduce a reference meta-model for simulation in process mining. We derive the meta-model using the provided insights from process mining and the required parameters from the simulation techniques for simulating processes, i.e., Discrete Event Simulation (DES). This model enables the creation of process simulations and the comparison of approaches in relation to the process aspects under consideration. We illustrate the use of the model in practice by developing an automatic simulation model generation approach based on the reference model.