Discovering Hierarchical Multi-Instance Business Processes From Event Logs.

Process discovery aims to extract descriptive process models from event logs. To date, various process discovery algorithms have been proposed for different application settings. However, most of them meet challenges in handling event logs produced from hierarchical multi-instance business processes, in which multiple sub-process instances are invoked by the execution of a parent process. To address the problem, a novel approach is presented to support the discovery of hierarchical multi-instance process models. Specifically, taking event logs with multi-instance information as input, the detailed implementation of our method can be generally divided into four steps: nesting relation detection, hierarchical event log construction, sub-process case identification, and hierarchical multi-instance model discovery. We have implemented our approach properly as plugins in the openly accessible ProM toolkit, and compared its performance against the state-of-the-art process discovery approaches over six publicly available event logs. Based on the experimental result, it is demonstrated that the proposed approach can effectively discover hierarchical multi-instance process models with better quality.