On the Formulation of Performant SPARQL Queries

TL;DR

This paper introduces five heuristics for writing efficient SPARQL queries, supported by formal semantics and empirical testing, leading to improved performance across multiple RDF stores.

Contribution

It proposes a set of five heuristics for optimizing SPARQL queries, grounded in formal semantics, and validates their effectiveness through empirical experiments.

Findings

Performance improvements observed in 6 RDF stores

Heuristics lead to more efficient query execution

Formal grounding ensures reliable optimization

Abstract

The combination of the flexibility of RDF and the expressiveness of SPARQL provides a powerful mechanism to model, integrate and query data. However, these properties also mean that it is nontrivial to write performant SPARQL queries. Indeed, it is quite easy to create queries that tax even the most optimised triple stores. Currently, application developers have little concrete guidance on how to write "good" queries. The goal of this paper is to begin to bridge this gap. It describes 5 heuristics that can be applied to create optimised queries. The heuristics are informed by formal results in the literature on the semantics and complexity of evaluating SPARQL queries, which ensures that queries following these rules can be optimised effectively by an underlying RDF store. Moreover, we empirically verify the efficacy of the heuristics using a set of openly available datasets and corresponding SPARQL queries developed by a large pharmacology data integration project. The experimental results show improvements in performance across 6 state-of-the-art RDF stores.