Creating a Recommender Plug-In for Enterprise Architecture Models

Abstract: Information Technology (IT) has evolved over the decades, where its role and impact have transitioned from being a tactical tool to a more strategic one for driving business strategies to transform organizations. The right alignment between IT strategy and business has become a compelling factor for Chief Information Officer (CIO)s where misalignment could lead to a degradation of organization performance and slow them down in the landscape of market competition. Enterprise Architecture (EA) in practice is one of the approaches where this alignment can be achieved. Through Enterprise Modeling (EM), EA supports all the stakeholders (Business and IT) of an organization to have a common understanding for communication of information, making decisions, and coordinating actions. EM results in EA models that are composed of enterprise components and relationships, that are stored in a repository. Over time, the repository grows as architects design new models or update existing models with complex structures. This opens up new avenues of research where the data can be used to provide intelligence. One such field is Recommender Systems (RS) which are software tools and techniques to provide meaningful suggestions to the user. RS can take different forms in the EM domain. For instance, identifying semantically similar components [8] or modeling assistance during the EM process [40]. Each form of recommendation can be enhanced with sophisticated recommendation models over time implemented in different technologies. We focus on the latter problem in this thesis where we implement a recommender framework that contains a robust architecture that easily integrates with different RS. We present the robustness and the ease of integration criteria for the framework. The framework is distributed as a plugin for Archi, a modeling toolkit used by Enterprise Architects to create EA models.