University Research Graph Database For Efficient Multi-Perspective Data Analysis Using Neo4j

Abstract

In general, research-related data are modeled using a relational database optimized for transaction processing. In many cases, this solution is effective and efficient enough to answer basic queries and simple reporting requirements. However, when users request a more-in-depth, more expansive, multi-perspective, and sometimes more abstract analysis, the relational database struggles to provide answers. This study proposes a research graph database implemented using neo4j as an effort to answer the problems. The database consists of a core model and an extension model. The core model represents scientific articles-related data loaded with real data scraped from Google Scholar. The extension model indicates research and community engagement activities done by researchers loaded manually. The database enables the university to analyze researchers’ individual and collaborative performances with fellow researchers inside and outside universities. The study concludes that the research graph database implementation is more efficient in answering similar questions than the relational database implementation.