Learning Complex Activity Preconditions in Process Mining
نویسندگان
چکیده
The availability of automatic support may determine the successful accomplishment of many real-world procedures. However, the underlying process models are too complex for writing and setting up them manually, and even standard machine learning approaches may be unable to infer them. Additionally, suitable conditions may that determine whether some tasks are to be carried out or not. These conditions may be in turn very complex, involving sequential relationships that take into account the past history of the process. This paper presents a First-Order Logic approach to learn complex process models extended with conditions. It combines two powerful Inductive Logic Programming systems. The overall system was applied to learning the daily routines of the user of a smart environment, for predicting his needs and comparing the actual situation with the expected one. Promising results have been obtained, that proved its efficiency and effectiveness, and with a domain-specific dataset.
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