Automated Discovery of Business Process Simulation Models from Event Logs
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Simulation is a versatile technique for quantitative analysis of business processes. It allows analysts to detect bottlenecks and to estimate the performance of a process under multiple scenarios. However, the discovery, validation, and tuning of business process simulation models is cumbersome and error-prone. It requires manual iterative refinement of the process model and simulation parameters in order to match the observed reality as closely as possible. Modern information systems store detailed execution logs of the business processes they support. Previous work has shown that these logs can be used to discover simulation models. However, existing methods for log-based discovery of simulation models do not seek to optimize the accuracy of the resulting models. Instead they leave it to the user to manually tune the simulation model to achieve the desired level of accuracy. This article presents an accuracy optimized method to automatically discover business process simulation models from execution logs. The method decomposes the problem at hand into a series of steps with associated configuration parameters. A Bayesian hyper-parameter optimization method is then used to search through the space of possible configurations with the goal of maximizing the similarity between the behavior of the simulation model and the behavior observed in the log. The method has been implemented as a tool and evaluated using three execution logs from different domains.
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