Abstract
Manually building process models is complex, costly and error-prone. Hence, the interest in process mining. Incremental adaptation of the models, and the ability to express/learn complex conditions on the involved tasks, are also desirable. First-order logic provides a single comprehensive and powerful framework for supporting all of the above. This paper presents a First-Order Logic incremental method for inferring process models. Its efficiency and effectiveness were proved with both controlled experiments and a real-world dataset.
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Ferilli, S., Redavid, D., Esposito, F. (2015). Logic-Based Incremental Process Mining. In: Bifet, A., et al. Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science(), vol 9286. Springer, Cham. https://doi.org/10.1007/978-3-319-23461-8_17
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DOI: https://doi.org/10.1007/978-3-319-23461-8_17
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