Revising process models through inductive learning

Discovering the Business Process (BP) model underpinning existing practices through analysis of event logs, allows users to understand, analyse and modify the process. But, to be useful, the BP model must be kept in line with practice throughout its lifetime, as changes occur to the business objectives, technologies and quality programs. Current techniques require users to manually revise the BP to account for discrepancies between the practice and the model, which is a laborious, costly and error prone task. We propose an automated approach for resolving such discrepancies by minimally revising a BP model to bring it in line with the activities corresponding to its executions, based on a non-monotonic inductive learning system. We discuss our implementation of this approach and demonstrate its application to a case-study. We further contrast our approach with existing BP discovery techniques to show that BP revision offers significant advantages over BP discovery in practical use.