AIMED: An automatic and incremental approach for business process model repair under concept drift.

Real-life business processes may change over time in response to new business requirements, market changes, new policies or regulations, etc., which is called concept drift in the data mining area. How to identify and deal with the concept drift problem is a significant challenge in business process mining. Currently, the research mainly focuses on the problems of concept drift detection. However, the proposed approaches do not intuitively explain how the process changes. In this paper, we provide an integrated framework whose core technique is an A utomatic and I ncremental approach for business process M odel r E pair under concept D rift (AIMED). AIMED streamlines the functions of concept drift detection, localization and model repair. More specifically, when concept drift is detected, it updates the process model automatically by precisely localizing the sub-structure of the process model that concept drift affects and updating this sub-structure accordingly. Concept drift is explained intuitively by presenting the repaired model. In particular, AIMED can resist the noise that greatly affects the performance of current concept drift detection and process model repair techniques. The properties of AIMED are theoretically proven. We also conduct extensive experiments on synthetic logs as well as real-life logs. The experiment results show that AIMED outperforms the state-of-the-art methods in both concept drift detection and process model repair and works well even when there is noise in logs. [ABSTRACT FROM AUTHOR]