RDF Query Processing: Relational Vs. Graph Approach

Abstract

AbstractThe data volume for the resource description framework (RDF) is growing rapidly. To query this large amount of data, two types of query processing approaches are there: the relational approach and graph-based approach. The relational approach offers better scalability and good security, but it suffers from query joins. The graph-based approach helps to eliminate query joins. Both relational and graph-based approach have their own advantages. This paper presents the performance analysis of relational database management system (RDBMS) and graph database management system (GDBMS) in terms of query processing. The evaluation of query execution time (QET) is carried out for the same benchmark linked observation data (LOD). A detailed comparison is carried out using two popular graph-based tools, Neo4j, and DGraph with relational database PostgreSQL. Neo4j uses cipher-query language, and DGraph uses GraphQL+−, and PostgreSQL uses standard SQL language for the RDBMS. The experiments are performed for four different types of queries. It has been observed that RDBMS outperforms GDBMS for star and range queries, and graph database works well with join and projection queries. We have extended our evaluation for the comparison of available graph tools, and it has been found that DGraph outperforms Neo4j for all the types of queries and works 2.5% faster for the query execution time as well.KeywordsCypherGraph databaseGraphQL+−RDFSQL