Concept drift detection in event logs using statistical information of variants

In recent years, business process management (BPM) has been highly regarded as an improvement in the efficiency and effectiveness of organizations. Extracting and analyzing information on business processes is an important part of this structure. But these processes are not sustainable over time and may change for a variety of reasons, such as the environment and human resources. These changes in processes are referred to as concept drift. The discovery of concept drifts is one of the challenges in business process management. These drifts may occur suddenly, gradually, periodically or incrementally. This paper proposes an algorithm for identifying concept drifts in event log, based on the distribution of trace variants in the execution of processes. In this method, by moving two windows on the event log, two feature vectors are derived from the two windows trace variants. Then variants of the two windows are compared by applying statistical tests and finally the drifts are identified. Experiments on artificial databases show the correctness of the method and its superiority to the previous methods.