H2RDF+ : An Efficient Data Management System for Big RDF Graphs

The proliferation of data in RDF format has resulted in the emergence of a plethora of specialized management systems. While the ability to adapt to the complexity of a SPARQL query {given their inherent diversity {is crucial, current approaches do not scale well when faced with substantially complex, non-selective joins, resulting in exponential growth of execution times. In this demonstration we present H2RDF+, an RDF store that efficiently performs distributed Merge and Sort-Merge joins using a multiple-index scheme over HBase indexes. Through a greedy planner that incorporates our cost-model, it adaptively commands for either single or multi-machine query execution based on join complexity. In this paper, we present its key scientific contributions and allow participants to interact with an H2RDF+ deployment over a Cloud infrastructure. Using a web-based GUI we allow users to load different datasets (both real and synthetic), apply any query (custom or predefined) and monitor its execution. By allowing real-time inspection of cluster status, response times and committed resources the audience will evaluate the validity of H2RDF+'s claims and perform direct comparisons to two other state-of-the-art RDF stores.