Logic-based incremental process mining

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.