Heuristic mining revamped : an interactive, data-aware, and conformance-aware miner

Conference Contribution

Mannhardt, F., de Leoni, M. & Reijers, H.A. (2017). Heuristic mining revamped : an interactive, data-aware, and conformance-aware miner. In A. Kumar, M. Weske, H. Leopold, W. van der Aalst, J. Mendling, B. Pentland & R. Clarisó (Eds.), Proceedings of the BPM Demo Track and BPM Dissertation Award, co-located with 15th International Conference on Business Process Management (BPM 2017), Barcelona, Spain, September 13, 2017 (pp. 1-5). (CEUR Workshop Proceedings, No. 1920). CEUR-WS.org. In Scopus Cited 0 times. Read more: Medialink/Full text



Process discovery methods automatically infer process models based on events logs that are recorded by information systems. Several heuristic process discovery methods have been proposed to cope with less structured processes and the presence of noise in the event log. However, (1) a large parameter space needs to be explored, (2) several of the many available heuristics can be chosen from, (3) data attributes are not used for discovery, (4) discovered models are not visualized as described in literature, and (5) existing tools do not give reliable quality diagnostics for discovered models. We present the interactive Data-aware Heuristics Miner (iDHM), a modular tool that attempts to address those five issues. The iDHM enables quick interactive exploration of the parameter space and several heuristics. It uses data attributes to improve the discovery procedure and provides built-in conformance checking to get direct feedback on the quality of the model. It is the first tool that visualizes models using the concise Causal Net (C-Net) notation. We provide a walk-through of the iDHM by applying it to a large event log with hospital billing information.