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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 1/2017

19.03.2016 | Regular Paper

Representation and analysis of enterprise models with semantic techniques: an application to ArchiMate, e3value and business model canvas

verfasst von: Artur Caetano, Gonçalo Antunes, João Pombinho, Marzieh Bakhshandeh, José Granjo, José Borbinha, Miguel Mira da Silva

Erschienen in: Knowledge and Information Systems | Ausgabe 1/2017

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Abstract

Enterprise models assist the governance and transformation of organizations through the specification, communication and analysis of strategy, goals, processes, information, along with the underlying application and technological infrastructure. Such models cross-cut different concerns and are often conceptualized using domain-specific modelling languages. This paper explores the application of graph-based semantic techniques to specify, integrate and analyse multiple, heterogeneous enterprise models. In particular, the proposal described in this paper (1) specifies enterprise models as ontological schemas, (2) uses transformation mapping functions to integrate the ontological schemas and (3) analyses the integrated schemas with graph querying and logical inference. The proposal is evaluated through a scenario that integrates three distinct enterprise modelling languages: the business model canvas, e3value, and the business layer of the ArchiMate language. The results show, on the one hand, that the graph-based approach is able to handle the specification, integration and analysis of enterprise models represented with different modelling languages and, on the other, that the integration challenge resides in defining appropriate mapping functions between the schemas.
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Metadaten
Titel
Representation and analysis of enterprise models with semantic techniques: an application to ArchiMate, e3value and business model canvas
verfasst von
Artur Caetano
Gonçalo Antunes
João Pombinho
Marzieh Bakhshandeh
José Granjo
José Borbinha
Miguel Mira da Silva
Publikationsdatum
19.03.2016
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 1/2017
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-016-0933-0

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