A Logic Framework for Incremental Learning of Process Models

Article type: Research Article

Authors: Ferilli, Stefano | Esposito, Floriana

Affiliations: Dipartimento di Informatica – Università di Bari, Italy. {ferilli, esposito}@di.uniba.it

Note: [] Address for correspondence: Dipartimento di Informatica – Università di Bari, Italy

Abstract: 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.

Keywords: Workflow, Process Models, First Order Logic, Relational Learning

DOI: 10.3233/FI-2013-951

Journal: Fundamenta Informaticae, vol. 128, no. 4, pp. 413-443, 2013