Incremental Declarative Process Mining with WoMan

Contemporary society is pervaded by complex processes. A company's success may depend on the proper definition, handling and management of its processes. Automated process management is fundamental to efficiently, effectively and economically carry out complex processes. In particular, emph{process discovery} is fundamental to automatically obtain process models from available executions of processes, because manually building these models is complex, costly and error-prone. Very important is incrementality in learning and adapting the models. This is not trivial, especially if the model includes multi-perspectiveness and guards. This paper describes the incremental process discovery strategy of the extsc{WoMan} framework for workflow management, based on First-Order Logic. It is emph{fully} and emph{inherently} incremental, it is more expressive than standard formalisms adopted in the literature, and ensures strict adherence to the observed practices. The incremental behavior of WoMan is also analyzed, reporting several experiments that show its effectiveness and efficiency.