Towards Intelligent Model Management: An Exploratory Study and Road-mapping

With data science entering various domains, new branches are emerging due to the extraction of latent knowledge from each domain's data. Model-based engineering and modeling are no exceptions. Now is the time to open a new chapter in this field by leveraging advanced artificial intelligence techniques. As the number and complexity of models increase, NP-complete problems arise that cannot be effectively addressed through deterministic management solutions. An effective way to address these challenges is by applying non-deterministic intelligent methodologies and data science-derived solutions. The increasing number of models and the formation of large model repositories necessitate intelligent model management, which aims to recognize hidden patterns and knowledge within these repositories using data science, machine learning techniques, and statistical and probabilistic methods for reuse. Despite the progress made in this area, both theoretically and practically, intelligent model management has not yet secured a prominent place in the body of knowledge of model-driven engineering. In this paper, we aim to clarify the exact position of intelligent model management by providing precise definitions, distinguishing it from conventional management, and identifying associated challenges. The above objective outlines the research approach in this area, making it easy for researchers to comprehend the procedures they employ for conducting their investigations.