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, human resources, capital market changes, seasonal, and climate changes. These changes in business processes are referred to as concept drift in event logs. 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 sudden concept drifts in event logs that are created by BPM. Each execution of the process instance follows a specific path in the process model called a trace, all traces that follow the same path in process model are called a variant. The proposed algorithm is based on the distribution of trace variants in the execution of processes. In this method, by moving two sliding windows on the event log, two feature vectors are derived from the two windows trace variants, these windows are named reference and detection windows. Then variants of the two windows are compared by applying statistical G-test and finally the drifts are identified.  In statistics, G-test is likelihood-ratio or maximum likelihood statistical significance test. Experiments on artificial databases show the correctness of the method and its superiority to the previous methods. In the proposed method, the detection accuracy is 0.06% better than state-of-the-art methods on average