Efficient and Adaptable Query Workload-Aware Management for RDF Data

The Resource Description Framework (RDF) is a flexible model for representing information about resources in the web. With the increasing amount of RDF data which is becoming available, efficient and scalable management of RDF data has become a fundamental challenge to achieve the Semantic Web vision. We present 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. The aim of the vertical partitioning phase is to reduce the number of join operations in the query evaluation process while the adjustment phase aims to maintain the efficiency of the performance of the query processing by adapting the underlying schema to cope with the dynamic nature of the 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.