Kanban-based framework for analysis of heterogeneous academic data

Bibliometric techniques are widely used to study the factors leading to successful research, though this is not without its challenges. One notable problem is that academic data is very diverse, and involves complex interactions between many different entities and players. In this paper, a novel framework for analyzing heterogeneous academic data is proposed. While such a framework would have many different applications, this paper focuses on the design of an academic recommendation system, which is one interesting use case for this framework.