Automated discovery of business process simulation models from event logs

Manuel Camargo, Marlon Dumas, Oscar González-Rojas
2020 Decision Support Systems  
Simulation is a versatile technique for quantitative analysis of business processes. It allows analysts 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
more » ... 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 discover business process simulation models from execution logs. The method decomposes the problem into a series of steps with associated configuration parameters. A hyper-parameter optimization method is then used to search through the space of possible configurations so as to maximize 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 logs from different domains. Journal Pre-proof J o u r n a l P r e -p r o o f ( / )  implies partially supported, for example, supported but not in an automated manner. Journal Pre-proof https://github.com/AdaptiveBProcess/Simod
doi:10.1016/j.dss.2020.113284 fatcat:7fdqv5nstvg3nbxxgpgngeo2vq