Stochastic Process Model-Log Quality Dimensions: An Experimental Study

Stochastic process models are a type of model that explicitly include elements of probability in describing an organization, facilitating different modes of analysis and simulation. Having obtained models of an organizational process, say through process mining, using them well depends on understanding their quality, and being able to compare different models. There may not be a single optimal stochastic model for a process, but trade-offs between models, decided by their intended use. Reasoning about trade-offs in a precise way requires quantitative measures, and an understanding of how these measures relate, including whether they capture independent underlying properties. This paper is an empirical investigation of measures for stochastic process models built from real-life logs. The experimental design assembles a large collection of models built both randomly and by discovery techniques. A wide spectrum of candidate measures, drawn from and inspired by the process mining literature, are applied using these models. Based on this analysis, three stochastic quality dimensions are proposed: adhesion, entropy and simplicity.