Discovering Object-Centric Process Simulation Models

International Conference on Process Mining (ICPM), pp. 81–88

Abstract

Process simulation assesses the impact of changing environmental parameters on a process. To obtain realistic simulation models, process mining techniques can be deployed for a log-based discovery. Such discovery techniques usually rely on a fixed case notion, falling short in capturing the entangled nature of real organizational processes as an interplay of objects and subprocesses. Yet there is a need for such methods, given the requirement for information systems to foresee and adapt to changing environments in an online setting and in a holistic manner. In this work, we approach this research need by elaborating a method for simulation model discovery that is based on the object-centricity paradigm. To implement object-centric simulation, some intrinsic challenges have to be overcome. These include, first, the parametrizable generation of sets of objects having predefined interrelations that structure possible behavior. Second, the generated objects have to be synchronized and routed through a control-flow model. We outline these challenges, describe our solution approach, and evaluate the quality of both object generation and behavior.

Authors 3

1. Benedikt Knopp Aachen Chair of Process and Data Science

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Chair of Process and Data Science,Aachen,Germany

   Chair of Process and Data Science, RWTH Aachen University, Aachen, Germany

2. Mahsa Pourbafrani Aachen Chair of Process and Data Science

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Chair of Process and Data Science,Aachen,Germany

   Chair of Process and Data Science, RWTH Aachen University, Aachen, Germany

3. Wil M. P. van der Aalst Aachen Chair of Process and Data Science

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Chair of Process and Data Science,Aachen,Germany

   Chair of Process and Data Science, RWTH Aachen University, Aachen, Germany