Distributed SPARQL Query Processing Based on Range Partitioning

Abstract

Distributed SPARQL query processing is considered as a desirable solution to improve the query performance for large RDF graphs. Most previous works are based on data or query decomposition, which often results in poor scalability due to communication overheads between servers. We propose a new approach to distributed SPARQL processing based on range partitioning which needs just a single communication cycle. Preliminary experimental results for LUBM show almost linear speedups for most queries.