Partially ordered stochastic conformance checking

Abstract Process mining aids organisations in improving their operational processes by providing visualisations and algorithms that turn event data into insights. How often behaviour occurs in a process—the stochastic perspective—is important for simulation, recommendation, enhancement and other types of analysis. Although the stochastic perspective is important, the focus is often on control flow. Stochastic conformance checking techniques assess the quality of stochastic process models and/or event logs with one another. In this paper, we address three limitations of existing stochastic conformance checking techniques: inability to handle uncertain event data (e.g. events having only a date), exponential blow-up in computation time due to the analysis of all interleavings of concurrent behaviour and the problem that loops that can be unfolded infinitely often. To address these challenges, we provide bounds for conformance measures and use partial orders to encode behaviour. An open-source implementation is provided, which we use to illustrate and evaluate the practical feasibility of the approach.