Discovering Hierarchical Processes Using Flexible Activity Trees for Event Abstraction
This addresses the problem of managing complexity in process mining for domains like healthcare, though it appears incremental as it builds on existing hierarchical ideas.
The paper tackles the challenge of discovering hierarchical process models from complex event logs with interleaved subprocesses, proposing FlexHMiner, which generates models that compare favorably to non-hierarchical approaches in evaluations on seven real-life logs.
Processes, such as patient pathways, can be very complex, comprising of hundreds of activities and dozens of interleaved subprocesses. While existing process discovery algorithms have proven to construct models of high quality on clean logs of structured processes, it still remains a challenge when the algorithms are being applied to logs of complex processes. The creation of a multi-level, hierarchical representation of a process can help to manage this complexity. However, current approaches that pursue this idea suffer from a variety of weaknesses. In particular, they do not deal well with interleaving subprocesses. In this paper, we propose FlexHMiner, a three-step approach to discover processes with multi-level interleaved subprocesses. We implemented FlexHMiner in the open source Process Mining toolkit ProM. We used seven real-life logs to compare the qualities of hierarchical models discovered using domain knowledge, random clustering, and flat approaches. Our results indicate that the hierarchical process models that the FlexHMiner generates compare favorably to approaches that do not exploit hierarchy.