High-performance, Distributed Dictionary Encoding of RDF Datasets

In this work we propose a novel approach for RDF (Resource Description Framework) dictionary encoding that employs a parallel RDF parser and a distributed dictionary data structure, exploiting RDF-specific optimizations. In contrast with previous solutions, this approach exploits the Partitioned Global Address Space (PGAS) programming model combined with active messages. We evaluate the performance of our dictionary encoder in our RDF database, GEMS (Graph Engine for Multithreaded Systems), and provide an empirical comparison against previous approaches. Our comparison shows that our dictionary encoder scales significantly better and achieves higher performance than the current state of the art, providing a key element for the realization of a more efficient RDF database.