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Using Knowledge Graphs to Detect Enterprise Architecture Smells
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering. The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark.ORCID iD: 0000-0003-0478-9347
2021 (English)In: IFIP Working Conference on The Practice of Enterprise Modeling, Springer Nature , 2021, p. 48-63Conference paper, Published paper (Refereed)
Abstract [en]

Hitherto, the concept of Enterprise Architecture (EA) Smells has been proposed to assess quality flaws in EAs and their models. Together with this new concept, a catalog of different EA Smells has been published and a first prototype was developed. However, this prototype is limited to ArchiMate and is not able to assess models adhering to other EA modeling languages. Moreover, the prototype is not integrate-able with other EA tools. Therefore, we propose to enhance the extensible Graph-based Enterprise Architecture Analysis (eGEAA) platform that relies on Knowledge Graphs with EA Smell detection capabilities. To align these two approaches, we show in this paper, how ArchiMate models can be transformed into Knowledge Graphs and provide a set of queries on the Knowledge Graph representation that are able to detect EA Smells. This enables enterprise architects to assess EA Smells on all types of EA models as long as there is a Knowledge Graph representation of the model. Finally, we evaluate the Knowledge Graph based EA Smell detection by analyzing a set of 347 EA models. 

Place, publisher, year, edition, pages
Springer Nature , 2021. p. 48-63
Series
Lecture Notes in Business Information Processing book series ; 432
Keywords [en]
Analysis, ArchiMate, Enterprise architecture, Knowledge graph, Model transformation, Graphic methods, Modeling languages, Odors, Analyse, Architecture analysis, Enterprise architecture modeling, Graph representation, Graph-based, Knowledge graphs, Quality flaws
National Category
Computer Systems Robotics and automation Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-313216DOI: 10.1007/978-3-030-91279-6_4ISI: 000797392200004Scopus ID: 2-s2.0-85119831729OAI: oai:DiVA.org:kth-313216DiVA, id: diva2:1665284
Conference
The Practice of Enterprise Modeling 14th IFIP WG 8.1 Working Conference, PoEM 2021, Riga, Latvia, November 24–26, 2021, Proceedings
Note

Part of proceedings: ISBN 9783030912789, QC 20230117

Available from: 2022-06-07 Created: 2022-06-07 Last updated: 2025-02-05Bibliographically approved

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Hacks, Simon

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • de-DE
  • en-GB
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Output format
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