Revising process models through inductive learning
Autor: | Maggi, F.M., Corapi, D., Russo, A., Lupu, E., Visaggio, G., Muehlen, zur, M., Su, J. |
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Přispěvatelé: | Process Science |
Jazyk: | angličtina |
Rok vydání: | 2011 |
Předmět: |
Process modeling
Computer science business.industry Process (engineering) Event (computing) Inductive bias Business process media_common.quotation_subject Machine learning computer.software_genre Task (project management) Information system Quality (business) Artificial intelligence business computer media_common |
Zdroj: | Business Process Management Workshops (BPM 2010 International Workshops and Education Track, Hoboken NJ, USA, September 13-15, 2010. Revised selected papers), 182-193 STARTPAGE=182;ENDPAGE=193;TITLE=Business Process Management Workshops (BPM 2010 International Workshops and Education Track, Hoboken NJ, USA, September 13-15, 2010. Revised selected papers) Business Process Management Workshops ISBN: 9783642205101 Business Process Management Workshops |
ISSN: | 1865-1348 |
Popis: | 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. |
Databáze: | OpenAIRE |
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