Unraveling Process Evolution by Handling Concept Drifts in Process Mining

Business processes change over time in response to changing circumstances, it is crucial for process managers to discover and understand such changes from event logs. While existing techniques in process mining cannot efficiently detect and locate concept drifts in uncompleted event logs. Hence, an appropriate method, which could prompt to unravel the process evolution, may greatly assist organizations to manage the flexibility and change of business processes in the context of business process management (BMP). In this paper, we first proposed an extensible feature and employed it with the sliding window technique and heuristic miner for detecting and locating concept drifts in uncompleted event logs. Then, Genetic Process Mining (GPM) was improved by the Weight Heuristic Miner (WHM) and Differential Evolution (DE) for mining the new process model of an evolving process. Finally, experimental results on four event logs which are created based on a real-world business process have validated the proposed method.