A Scalable Sparse Matrix-Based Join for SPARQL Query Processing

In this paper, we present gSMat, a SPARQL query engine for RDF datasets. It employs join optimization and data sparsity. We bifurcate gSMat into three submodules e.g. Firstly, SM Storage (Sparse Matrix-based Storage) which lifts the storage efficiency, by storing valid edges, introduces a predicate-based hash index on the storage and generate a statistic file for optimization. Secondly, Query Planner which holds Query Parser and Query Optimizer. The Query Parser module parses a SPARQL query and transformed it into a query graph and the latter generates the optimal query plan based on statistical input from SM Storage. Thirdly, Query Executor module executes query in an efficient manner. Lastly, gSMat evaluated by comparing with some well-known approaches like gStore and RDF3X on very large datasets (over 500 million triples). gSMat is proved as significantly efficient and scalable.