Using Event Logs for Local Correction of Process Models

Abstract

During the life-cycle of an Information System (IS) its actual behavior 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 behavior. 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 event log. The input data for this task is the initial process model (a Petri net) and an 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. Then 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 into several fragments. For each of the fragments 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 on artificial examples.