Toward a data scalable solution for facilitating discovery of science resources

Data-intensive science simultaneously derives from and creates the need for large quantities of data. As such, scientists increasingly need to discover and analyze new datasets from diverse sources. Beyond the sheer volume of data, issues posed by the resultant data heterogeneity are often overlooked. We postulate that heterogeneity challenges can be solved (at least in part) with the adoption of the Resource Description Framework (RDF), a graph-based data model. In turn, this requires scalable graph query systems for discovering and analyzing data. Consequently, we investigate GEMS, a graph engine for large-scale clusters. We describe the features of GEMS that make it suitable for answering graph queries and scaling to larger quantities of data. We evaluate GEMS' ability to answer real science-based queries over real-world, curated, science metadata. We also demonstrate GEMS' ability to scale to larger datasets using a benchmark.