Discovering Stochastic Petri Nets with Arbitrary Delay Distributions from Event Logs

Andreas Rogge-Solti, Wil M. P. van der Aalst, Mathias Weske

Abstract: Capturing the performance of a system or business process as accurately as possible is important, as models enriched with performance information provide valuable input for analysis, operational support, and prediction. Due to their computationally nice properties, memoryless models such as exponentially distributed stochastic Petri nets have earned much attention in research and industry. However, there are cases when the memoryless property is clearly not able to capture process behavior, e.g., when dealing with fixed time-outs.