A growing number of applications require combining SPARQL queries with generic graph search on RDF data. However, the lack of procedural capabilities in SPARQL makes it inappropriate for graph analytics. Moreover, RDF engines focus on SPARQL query evaluation whereas graph management frameworks perform only generic graph computations. In this work, we bridge the gap by introducing SPARTex, an RDF analytics framework based on the vertex-centric computation model. In SPARTex, user-defined vertex centric programs can be invoked from SPARQL as stored procedures. SPARTex allows the execution of a pipeline of graph algorithms without the need for multiple reads/writes of input data and intermediate results. We use a cost-based optimizer for minimizing the communication cost. SPARTex evaluates queries that combine SPARQL and generic graph computations orders of magnitude faster than existing RDF engines. We demonstrate a real system prototype of SPARTex running on a local cluster using real and synthetic datasets. SPARTex has a real-time graphical user interface that allows the participants to write regular SPARQL queries, use our proposed SPARQL extension to declaratively invoke graph algorithms or combine/pipeline both SPARQL querying and generic graph analytics.
|Title of host publication
|Proceedings of the VLDB Endowment
|Published - Aug 31 2015