Efficient and Adaptable Query Workload-Aware Management for RDF Data

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Abstract

This paper presents a flexible and adaptable 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 two-phase approach for designing efficient tailored but flexible storage solution for RDF data based on its query workload, namely: 1) a workload-aware vertical partitioning phase. 2) an automated adjustment 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.