SAMUEL: A Sharing-based Approach to processing Multiple SPARQL Queries with MapReduce

The volume of RDF data is now growing tremendously. It is thus considered prudent to store and process massive RDF data with distributed SPARQL engines instead of relying on a singlemachine system.Many sophisticated index and partitioning schemes have also been proposed to support SPARQL query evaluations. However, existing SPARQL engines have mainly followed oneat-a-time scheme so that query evaluation is focused only on processing each query separately. We showcase SAMUEL, a distributed SPARQL engine that simultaneously evaluates many SPARQL queries for a massive RDF dataset with MapReduce. SAMUEL provides an efficient optimization algorithm to evaluate many SPARQL queries simultaneously in a shared and balanced way. Extensive experiments present that without any sophisticated partitioning or index mechanisms, our approach significantly outperforms other MapReduce-based SPARQL engines as well as an ad-hoc query engine equipped with various indexes and partitioning tools for evaluating multiple SPARQL queries.