A Framework for Analyzing Academic Data

In recent years, academic data is growing rapidly in terms of volume, variety, velocity, value, and reliability. Analysing and managing it is therefore more difficult and challenging. Discovery of academic data can yield great benefits to the scientific community. In addition, academic data analysis helps to plan and orient development for research and industry. Fortunately, there are many scholarly data resources available such as the DBLP Computer Science Bibliography, ResearchGate, CiteSeer, and Google Scholar, which enable users easy access and analysis. In this paper, we propose a framework for analysing representative research issues in an academic context. We are currently in the process of building a system for collaborator recommendation, academic venue recommendation, expert finding, and group expert prediction, which are the primary issues in an academic context.