Under consideration for publication in Knowledge and Information Systems Compressed Vertical Partitioning for Efficient RDF Management1

Abstract. The Web of Data has been gaining momentum in recent years. This leads to increasingly publish more and more semi-structured datasets following, in many cases, the RDF (Resource Description Framework) data model based on atomic triple units of subject, predicate, and object. Although it is a very simple model, specific compres-sion methods become necessary because datasets are increasingly larger and various scalability issues arise around their organization and storage. This requirement is even more restrictive in RDF stores because efficient SPARQL solution on the compressed RDF datasets is also required. This article introduces a novel RDF indexing technique that supports efficient SPARQL solution in compressed space. Our technique, called k2-triples, uses the predicate to vertically partition the dataset into disjoint subsets of pairs (subject, ob-ject), one per predicate. These subsets are represented as binary matrices of subjects × objects in which 1-bits mean that the corresponding triple exists in the dataset. This model results in very sparse matrices, which are efficiently compressed using k2-trees. We enhance this model with two compact indexes listing the predicates related to eac