Abstract
This paper addresses the problem of discovering business process models from event logs. Existing approaches to this problem strike various tradeoffs between accuracy and understandability of the discovered models. With respect to the second criterion, empirical studies have shown that block-structured process models are generally more understandable and less error-prone than unstructured ones. Accordingly, several automated process discovery methods generate block-structured models by construction. These approaches however intertwine the concern of producing accurate models with that of ensuring their structuredness, sometimes sacrificing the former to ensure the latter. In this paper we propose an alternative approach that separates these two concerns. Instead of directly discovering a structured process model, we first apply a well-known heuristic that discovers more accurate but sometimes unstructured (and even unsound) process models, and then transform the resulting model into a structured one. An experimental evaluation shows that our “discover and structure” approach outperforms traditional “discover structured” approaches with respect to a range of accuracy and complexity measures.
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Notes
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- 2.
Available from http://apromore.org/platform/tools.
- 3.
This collection originally counted 59 models, but we discarded five duplicates.
- 4.
The original labels are replaced with letters for the sake of compactness.
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Acknowledgments
This research is partly funded by the Australian Research Council (grant DP150103356) and the Estonian Research Council (grant IUT20-55).
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Augusto, A., Conforti, R., Dumas, M., La Rosa, M., Bruno, G. (2016). Automated Discovery of Structured Process Models: Discover Structured vs. Discover and Structure. In: Comyn-Wattiau, I., Tanaka, K., Song, IY., Yamamoto, S., Saeki, M. (eds) Conceptual Modeling. ER 2016. Lecture Notes in Computer Science(), vol 9974. Springer, Cham. https://doi.org/10.1007/978-3-319-46397-1_25
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