OntoDS: An Ontology-Aware Distributed Storage Scheme for RDF Graphs

Abstract

With the development of the Semantic Web, the amount of RDF data has been increasing rapidly. It is no longer feasible to store entire data sets on a single machine, and still be able to access the data at reasonable performance. Consequently, the requirement for clustered RDF database systems is becoming more and more important. At the same time, the native storage scheme of RDF data is less mature in many aspects compared with relational storage scheme. SQL-on-Hadoop is a distributed relational database engine for big data with many factors, which uses Hadoop to improve the fault tolerance of the system and is fully transactional. However, currently, there is no SQL-on-Hadoop relational database that realizes a subsystem for RDF data storage. In this paper, we propose an Ontology-aware Distributed Storege scheme for RDF, called OntoDS, which modifies the relational RDF data storage scheme DB2RDF to build a novel scheme for RDF data and optimizes the partitioning of RDF graphs by distributing RDF triples based on ontologies to meet the need for RDF graph data storage and query load. The experimental results on the benchmark datasets show that our distributed RDF storage scheme is about 1–1.5 times faster than the state-of-the-art native storage schemes.