Program Committee

Abstract

RDFox is a new materialisation-based RDF system currently being developed at Oxford University. The system is currently RAM-based, and its algorithms have been designed to take full advantage of modern multi-core/processor systems. In my talk I will present an overview of some of the techniques we developed in the context of the RDFox project. In particular, I will discuss our algorithm that parallelises computation with very little overhead, I will present an overview of our lock-free indexes for RDF data, and I will discuss a novel incremental update algorithm. I will also briefly talk about some issues that we are currently working on, such as improving query planning and distributing data in a cluster of servers. The NPD Benchmark for OBDA Systems Davide Lanti, Martin Rezk, Mindaugas Slusnys, Guohui Xiao, and Diego Calvanese Faculty of Computer Science, Free University of Bozen-Bolzano, Italy Abstract. In Ontology-Based Data Access (OBDA), queries are posed over a high-level conceptual view, and then translated into queries over a potentially very large (usually relational) data source. The ontology is connected to the data sources through a declarative specification given in terms of mappings. Although prototype OBDA systems providing the ability to answer SPARQL queries over the ontology are available, a significant challenge remains: performance. To properly evaluate OBDA systems, benchmarks tailored towards the requirements in this setting are needed. OWL benchmarks, which have been developed to test the performance of generic SPARQL query engines, however, fail to evaluate OBDA specific features. In this work, we propose a novel benchmark for OBDA systems based on the Norwegian Petroleum Directorate (NPD). Our benchmark comes with novel techniques to generate, from available data, datasets of increasing size, taking into account the requirements dictated by the OBDA setting. We validate our benchmark on significant OBDA systems, showing that it is more adequate than previous benchmarks not tailored for OBDA. In Ontology-Based Data Access (OBDA), queries are posed over a high-level conceptual view, and then translated into queries over a potentially very large (usually relational) data source. The ontology is connected to the data sources through a declarative specification given in terms of mappings. Although prototype OBDA systems providing the ability to answer SPARQL queries over the ontology are available, a significant challenge remains: performance. To properly evaluate OBDA systems, benchmarks tailored towards the requirements in this setting are needed. OWL benchmarks, which have been developed to test the performance of generic SPARQL query engines, however, fail to evaluate OBDA specific features. In this work, we propose a novel benchmark for OBDA systems based on the Norwegian Petroleum Directorate (NPD). Our benchmark comes with novel techniques to generate, from available data, datasets of increasing size, taking into account the requirements dictated by the OBDA setting. We validate our benchmark on significant OBDA systems, showing that it is more adequate than previous benchmarks not tailored for OBDA.