Using Knowledge Graphs to Detect Enterprise Architecture Smells
2021 (Engelska)Ingår i: IFIP Working Conference on The Practice of Enterprise Modeling, Springer Nature , 2021, s. 48-63Konferensbidrag, Publicerat paper (Refereegranskat)
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.
Ort, förlag, år, upplaga, sidor
Springer Nature , 2021. s. 48-63
Serie
Lecture Notes in Business Information Processing book series ; 432
Nyckelord [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
Nationell ämneskategori
Datorsystem Robotik och automation Reglerteknik
Identifikatorer
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
Konferens
The Practice of Enterprise Modeling 14th IFIP WG 8.1 Working Conference, PoEM 2021, Riga, Latvia, November 24–26, 2021, Proceedings
Anmärkning
Part of proceedings: ISBN 9783030912789, QC 20230117
2022-06-072022-06-072025-02-05Bibliografiskt granskad