A Logic Framework for Incremental Learning of Process Models

• Autorzy: Ferilli S. , Esposito F.
• Języki publikacji: EN

Abstrakty

EN

Standardized processes are important for correctly carrying out activities in an organization. Often the procedures they describe are already in operation, and the need is to understand and formalize them in a model that can support their analysis, replication and enforcement. Manually building these models is complex, costly and error-prone. Hence, the interest in automatically learning them from examples of actual procedures. Desirable options are incrementality in learning and adapting the models, and the ability to express triggers and conditions on the tasks that make up the workflow. This paper proposes a framework based on First-Order Logic that solves many shortcomings of previous approaches to this problem in the literature, allowing to deal with complex domains in a powerful and flexible way. Indeed, First-Order Logic provides a single, comprehensive and expressive representation and manipulation environment for supporting all of the above requirements. A purposely devised experimental evaluation confirms the effectiveness and efficiency of the proposed solution.

Słowa kluczowe

EN

first-order logic; process models; relational learning; workflow

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2013
• Tom: Vol. 128, nr 4
• Strony: 413--443
• Opis fizyczny: Bibliogr. 33 poz., wykr.

Twórcy

autor

Ferilli S.

• Dipartimento di Informatica – Università di Bari, Italy

autor

Esposito F.

• Dipartimento di Informatica – Università di Bari, Italy

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