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Adaptation of Process Models -- A Semantic-based Approach.

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Title: Adaptation of Process Models -- A Semantic-based Approach.
Authors: Eisenbarth, Thomas1 eisenbarth@ds-lab.org, Lautenbacher, Florian1 lautenbacher@ds-lab.org, Bauer, Bernhard1 bauer@ds-lab.org
Source: Journal of Research & Practice in Information Technology. Feb2011, Vol. 43 Issue 1, p5-23. 19p.
Subjects: BPEL (Computer program language), Programming languages, Semantics, Comparative linguistics, Ontologies (Information retrieval)
Abstract: Semantic Business Process Management is an emerging research area to support enterprises achieving economic and strategic objectives and improving their daily business. However, the magnitude of changes which are required for models that capture the business processes is a challenge today. The necessity to change process models in order to stay synchronized with the reality is due to changing requirements within enterprises or external events like mergers and acquisitions, changing laws and reactions to changing markets. The approach we describe in this paper shows a way to automatically adapt existing process models when parts of a process have changed. We apply semantic technologies to the automatic planning approach for the adaptation. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Research & Practice in Information Technology is the property of Australian Computer Society Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Adaptation of Process Models -- A Semantic-based Approach.
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  Data: <searchLink fieldCode="AR" term="%22Eisenbarth%2C+Thomas%22">Eisenbarth, Thomas</searchLink><relatesTo>1</relatesTo><i> eisenbarth@ds-lab.org</i><br /><searchLink fieldCode="AR" term="%22Lautenbacher%2C+Florian%22">Lautenbacher, Florian</searchLink><relatesTo>1</relatesTo><i> lautenbacher@ds-lab.org</i><br /><searchLink fieldCode="AR" term="%22Bauer%2C+Bernhard%22">Bauer, Bernhard</searchLink><relatesTo>1</relatesTo><i> bauer@ds-lab.org</i>
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  Data: <searchLink fieldCode="DE" term="%22BPEL+%28Computer+program+language%29%22">BPEL (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22Programming+languages%22">Programming languages</searchLink><br /><searchLink fieldCode="DE" term="%22Semantics%22">Semantics</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+linguistics%22">Comparative linguistics</searchLink><br /><searchLink fieldCode="DE" term="%22Ontologies+%28Information+retrieval%29%22">Ontologies (Information retrieval)</searchLink>
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  Data: Semantic Business Process Management is an emerging research area to support enterprises achieving economic and strategic objectives and improving their daily business. However, the magnitude of changes which are required for models that capture the business processes is a challenge today. The necessity to change process models in order to stay synchronized with the reality is due to changing requirements within enterprises or external events like mergers and acquisitions, changing laws and reactions to changing markets. The approach we describe in this paper shows a way to automatically adapt existing process models when parts of a process have changed. We apply semantic technologies to the automatic planning approach for the adaptation. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Research & Practice in Information Technology is the property of Australian Computer Society Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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