An Extensible Framework for Query Optimization on TripleT-Based RDF Stores

The RDF data model is a key technology in the Linked Data vision. Given its graph structure, even relatively simple RDF queries often involve a large number of joins. Join evaluation poses a signicant performance challenge on all state-of-the-art RDF engines. TripleT is a novel RDF index data structure, demonstrated to be competitive with the current state-of-the-art for join processing. Query optimization on TripleT, however, has not been systematically studied up to this point. In this paper we investigate how the use of (i) heuristics and (ii) data statistics can contribute towards a more intelligent way of generating query plans over TripleT-based RDF stores. We propose a generic framework for query optimization, and show through an extensive empirical study that our framework consistently produces ecient query evaluation plans.