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.

Autore Pugliese

• Ferilli Stefano

Tutti gli autori

• FERILLI S.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2014

ISSN

2168-2216

ISBN

Non Disponibile

Numero di citazioni Wos

11

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

19

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile