Petri Net-Based Deviation Detection Between Event Logs and Process Models with Milestones

Abstract

The designation of milestone activities in business processes can restrict the effective occurrence of cases in logs and remove noises reasonably. A deviation detection method is presented between Petri nets and logs using milestone activities to improve the computation efficiency. An original model is constructed through some operations between two Petri nets, where one is the process model and the other is the log model created by mapping the activities in the case to the transitions. First, the transitions labeled with the milestones compose the synchronous transitions by synchronous composition. Next, other transitions with the same labels produce the log, model and synchronous transitions by product. Then, the transitions labeled with the activities that are merely observed in the model directly map to the transitions. Finally, the transitions labeled with the activities which are modeled only in the Petri net directly map to the model transitions, and the transitions labeled with no activities directly map to the invisible transitions. The new model is the search space for deviation detection. An algorithm is presented to detect the deviations between the Petri net based on the given cost and the case. The experimental results show that the more milestone activities in the business process, the higher computation efficiency of deviation detection.