Using Machine Learning Techniques for Evaluating the Similarity of Enterprise Architecture Models

Technical Paper
  • Vasil Borozanov
  • Simon HacksEmail author
  • Nuno Silva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11483)


Enterprises Architectures (EA) are facilitated to coordinate enterprise’s business visions and strategies successfully and effectively. The practitioners of EA (architects) communicate the architecture to other stakeholders via architecture models. We investigate the scenario where accepted architecture models are stored in a repository. We identified the problem of unnecessary repository expansion by adding model components with similar properties or behavior as already existing repository components. The proposed solution aims to find those similar components and to notify the architect about their existence.

We present two approaches for defining and combining similarities between EA model components. The similarity measures are calculated upon the properties of the components and on the context of their usage. We further investigate the behavior of similar architecture models and search for associations in order to obtain components that might be of interest. At the end, we provide a prototype tool for both generating requests and obtaining a result.


Enterprise architecture Model Graph Machine learning 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Research Group Software ConstructionRWTH Aachen UniversityAachenGermany
  2. 2.Department of Computer Science and EngineeringTechnical University of LisbonLisbonPortugal

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