P ^3 -Folder: Optimal Model Simplification for Improving Accuracy in Process Performance Prediction

Operational process models such as generalised stochastic Petri nets (GSPNs) are useful when answering performance queries on business processes (e.g. ‘how long will it take for a case to finish?’). Recently, methods for process mining have been developed to discover and enrich operational models based on a log of recorded executions of processes, which enables evidence-based process analysis. To avoid a bias due to infrequent execution paths, discovery algorithms strive for a balance between over-fitting and under-fitting regarding the originating log. However, state-of-the-art discovery algorithms address this balance solely for the control-flow dimension, neglecting possible over-fitting in terms of performance annotations. In this work, we thus offer a technique for performance-driven model reduction of GSPNs, using structural simplification rules. Each rule induces an error in performance estimates with respect to the original model. However, we show that this error is bounded and that the reduction in model parameters incurred by the simplification rules increases the accuracy of process performance prediction. We further show how to find an optimal sequence of applying simplification rules to obtain a minimal model under a given error budget for the performance estimates. We evaluate the approach with a real-world case in the healthcare domain, showing that model simplification indeed yields significant improvements in time prediction accuracy.