Using Event Logs for Local Correction of Process Models

During the life-cycle of an Information System (IS) its actual behaviour may not correspond to the original system model. However, to the IS support it is very important to have the latest model that reflects the current system behaviour. To correct the model, the information from the event log of the system may be used. In this paper, we consider the problem of process model adjustment (correction) using the information from an event log. The input data for this task are the initial process model (a Petri net) and the event log. The result of correction should be a new process model, better reflecting the real IS behavior than the initial model. The new model could be also built from scratch, for example, with the help of one of the known algorithms for automatic synthesis of the process model from an event log. However, this may lead to crucial changes in the structure of the original model, and it will be difficult to compare the new model with the initial one, hindering its understanding and analysis. It is important to keep the initial structure of the model as much as possible. In this paper, we propose a method for process model correction based on the principle of “divide and conquer”. The initial model is decomposed in several fragments. For each fragment its conformance to the event log is checked. Fragments which do not match the log are replaced by newly synthesized ones. The new model is then assembled from the fragments via transition fusion. The experiments demonstrate that our correction algorithm gives good results when it is used for correcting local discrepancies. The paper presents the description of the algorithm, the formal justification for its correctness, as well as the results of experimental testing by some artificial examples.

process mining, process model repair, Petri nets, process model decomposition, divide and conquer

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