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The Web of Data has been gaining momentum and this leads to increasinglypublish more semi-structured datasets following the RDF model, based on atomictriple units of subject, predicate, and object. Although it is a simple model,compression methods become necessary because datasets are increasingly largerand various scalability issues arise around their organization and storage.This requirement is more restrictive in RDF stores because efficient SPARQLresolution on the compressed RDF datasets is also required.  This article introduces a novel RDF indexing technique (called k2-triples)supporting efficient SPARQL resolution in compressed space. k2-triples, usesthe predicate to vertically partition the dataset into disjoint subsets ofpairs (subject, object), one per predicate. These subsets are represented asbinary matrices in which 1-bits mean that the corresponding triple exists inthe dataset. This model results in very sparse matrices, which are efficientlycompressed using k2-trees. We enhance this model with two compact indexeslisting the predicates related to each different subject and object, in orderto address the specific weaknesses of vertically partitioned representations.The resulting technique not only achieves by far the most compressedrepresentations, but also the best overall performance for RDF retrieval in ourexperiments. Our approach uses up to 10 times less space than a state of theart baseline, and outperforms its performance by several order of magnitude onthe most basic query patterns. In addition, we optimize traditional joinalgorithms on k2-triples and define a novel one leveraging its specificfeatures. Our experimental results show that our technique overcomestraditional vertical partitioning for join resolution, reporting the bestnumbers for joins in which the non-joined nodes are provided, and beingcompetitive in the majority of the cases.

Compressed Vertical Partitioning for Full-In-Memory RDF Management