  RDF query optimization is a challenging problem. Although considerable
factors and their impacts on query efficiency have been investigated, this
problem still needs further investigation. We identify that decomposing query
into a series of light-weight operations is also effective in boosting query
processing. Considering the linked nature of RDF data, the correlations among
operations should be carefully handled. In this paper, we present SGDQ, a novel
framework that features a partition-based Summary Graph Driven Query for
efficient query processing. Basically, SGDQ partitions data and models
partitions as a summary graph. A query is decomposed into subqueries that can
be answered without inter-partition processing. The final results are derived
by perform summary graph matching and join the results generated by all matched
subqueries. In essence, SGDQ combines the merits of graph match processing and
relational join-based query implementation. It intentionally avoids maintain
huge intermediate results by organizing sub-query processing in a summary graph
driven fashion. Our extensive evaluations show that SGDQ is an effective
framework for efficient RDF query processing. Its query performance
consistently outperforms the representative state-of-the-art systems.
