Additional information in Business processes: A pattern-based integration of natural language artefacts
Autor(en): | Bittmann, S. Metzger, D. Fellmann, M. Thomas, O. |
Herausgeber: | Reimer, U. Fill, H.-G. Karagiannis, D. |
Stichwörter: | Process engineering, Business process model; Business process modelling; Formal process models; Human interactions; Integrated representations; Process representation; Seamless integration; Semi-formal models, Mathematical models | Erscheinungsdatum: | 2014 | Herausgeber: | Gesellschaft fur Informatik (GI) | Journal: | Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI) | Volumen: | P225 | Startseite: | 137 | Seitenende: | 152 | Zusammenfassung: | Business process modelling initiatives frequently make use of semi-formal modelling languages for depicting the business processes and their control flows. While these representations are beneficial for the analysis, simulation and automatic execution of processes, they are not necessarily the best option to communicate process knowledge required by employees to execute the process. Hence, textual process representations and their transformation to semi-formal models gain importance. In this paper, a pattern-based modelling approach positioned in between the two extremes of informal text and semi-formal process models is derived. The patterns offer a basis for a seamless integration of natural language and business process models. In particular the business process modelling patterns, which have to rely on human interactions are focussed. For those patterns an integrated representation of information that support the manual execution is developed. The approach fosters the contribution by employees of the operative business, since it does not rely on classical modelling paradigms, but uses natural language for modelling business processes. |
Beschreibung: | Conference of Modellierung 2014 ; Conference Date: 19 March 2014 Through 21 March 2014; Conference Code:108090 |
ISBN: | 9783885796190 | ISSN: | 16175468 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908159452&partnerID=40&md5=3ac013323ef4fafd1eebfe91b8fddbcd |
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geprüft am 21.05.2024