Processing SPARQL Queries Over Distributed RDF Graphs

TL;DR

This paper introduces a distributed framework for processing SPARQL queries over large RDF graphs using partial evaluation and assembly, demonstrating superior performance and scalability through extensive experiments.

Contribution

It presents novel distributed algorithms for SPARQL query processing that improve efficiency and scalability over existing methods.

Findings

Outperforms state-of-the-art in system performance

Handles billions of triples efficiently

Validated through extensive real and benchmark data experiments

Abstract

We propose techniques for processing SPARQL queries over a large RDF graph in a distributed environment. We adopt a "partial evaluation and assembly" framework. Answering a SPARQL query Q is equivalent to finding subgraph matches of the query graph Q over RDF graph G. Based on properties of subgraph matching over a distributed graph, we introduce local partial match as partial answers in each fragment of RDF graph G. For assembly, we propose two methods: centralized and distributed assembly. We analyze our algorithms from both theoretically and experimentally. Extensive experiments over both real and benchmark RDF repositories of billions of triples confirm that our method is superior to the state-of-the-art methods in both the system's performance and scalability.