Detection and Classification of Concept Drift in Streaming Process Discovery

Streaming Process Discovery has attracted attention as a method to discover a process model in dynamic business processes. In particular, addressing concept drift that occurs within continuously incoming event logs has been emphasized as a critical challenge. Existing methods have mainly focused on the accuracy of detecting concept drift. However, they do not take into account the change type and the amount of change, leading to excessive updates of the process model even for minor changes. In this study, we propose a method that classifies concept drift into distinct types to reduce the number of updates and execution time appropriately. We conducted experiments using four different event logs. The results demonstrate that the proposed method outperforms existing methods, significantly reducing the number of updates and execution time.