Revising process models through inductive learning

F.M. Maggi, D. Corapi, A. Russo, E. Lupu, G. Visaggio

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

7 Citations (Scopus)
1 Downloads (Pure)


Discovering the Business Process (BP) model underpinning existing practices through analysis of event logs, allows users to understand, analyse and modify the process. But, to be useful, the BP model must be kept in line with practice throughout its lifetime, as changes occur to the business objectives, technologies and quality programs. Current techniques require users to manually revise the BP to account for discrepancies between the practice and the model, which is a laborious, costly and error prone task. We propose an automated approach for resolving such discrepancies by minimally revising a BP model to bring it in line with the activities corresponding to its executions, based on a non-monotonic inductive learning system. We discuss our implementation of this approach and demonstrate its application to a case-study. We further contrast our approach with existing BP discovery techniques to show that BP revision offers significant advantages over BP discovery in practical use.
Original languageEnglish
Title of host publicationBusiness Process Management Workshops (BPM 2010 International Workshops and Education Track, Hoboken NJ, USA, September 13-15, 2010. Revised selected papers)
EditorsM. Muehlen, zur, J. Su
Place of PublicationBerlin
ISBN (Print)978-3-642-20510-1
Publication statusPublished - 2011

Publication series

NameLecture Notes in Business Information Processing
ISSN (Print)1865-1348


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