This paper presents a
approach for achieving efficient and scalable management of RDF using relational databases. The main motivation behind our approach is that several benchmarking studies have shown that each RDF dataset requires a tailored table schema in order to achieve efficient performance during query processing. We present a
approach for designing efficient tailored but flexible storage solution for RDF data based on its query workload, namely: 1) a workload-aware
phase. 2) an automated
phase that reacts to the changes in the characteristics of the continuous stream of query workloads. We perform comprehensive experiments on two real-world RDF data sets to demonstrate that our approach is superior to the state-of-the-art techniques in this domain.