A chance for models to show their quality: Stochastic process model-log dimensions

Information Systems, vol. 124, pp. 102382

Abstract

Process models describe the desired or observed behaviour of organisations. In stochastic process mining, computational analysis of trace data yields process models which describe process paths and their probability of execution. To understand the quality of these models, and to compare them, quantitative quality measures are used. This research investigates model comparison empirically, using stochastic process models built from real-life logs. The experimental design collects a large number of models generated randomly and using process discovery techniques. Twenty-five different metrics are taken on these models, using both existing process model metrics and new, exploratory ones. The results are analysed quantitatively, making particular use of principal component analysis. Based on this analysis, we suggest three stochastic process model dimensions: adhesion, relevance and simplicity. We also suggest possible metrics for these dimensions, and demonstrate their use on example models.

Authors 5

1. Adam T. Burke corresponding

   Queensland University of Technology

   Affiliation as printed

   School of Information Systems, Queensland University of Technology, Brisbane, Australia

2. Sander J. J. Leemans Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH University, Aachen, Germany

3. Moe Thandar Wynn

   Queensland University of Technology

   Affiliation as printed

   School of Information Systems, Queensland University of Technology, Brisbane, Australia

4. Wil M. P. van der Aalst Aachen

   Queensland University of Technology · RWTH Aachen University

   Affiliation as printed

   RWTH University, Aachen, Germany

   School of Information Systems, Queensland University of Technology, Brisbane, Australia

5. Arthur H. M. ter Hofstede

   Queensland University of Technology

   Affiliation as printed

   School of Information Systems, Queensland University of Technology, Brisbane, Australia