WoMan: Logic-Based Workflow Learning and Management

Abstract

Workflow management is fundamental to efficiently, effectively, and economically carry out complex working and domestic activities. Manual engineering of workflow models is a complex, costly, and error-prone task. The WoMan framework for workflow management is based on first-order logic. Its core is an automatic procedure that learns and refines workflow models from observed cases of process execution. Its innovative peculiarities include incrementality (allowing quick learning even in the presence of noise and changed behavior), strict adherence to the observed practices, ability to learn complex conditions for the workflow components, and improved expressive power compared to the state of the art. This paper presents the entire algorithmic apparatus of WoMan, including translation and learning from a standard log format for case representation, import/export of workflow models from/into standard formalisms (Petri nets), and exploitation of the learned models for process simulation and monitoring. Qualitative and quantitative experimental evaluation shows the power and efficiency of WoMan, both in controlled and in real-world domains.

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