V. Borozanov, S. Hacks, N. Silva:
Using Machine Learning Techniques for Evaluating the Similarity of Enterprise Architecture Models

Abstract

Enterprises need a well-defined practice to coordinate their business visions and strategies successfully and effectively. That is the purpose of the Enterprise Architecture (EA). 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.

The paper will be presented at CAiSE’19, held in Rome, Italy.