A role-free approach to indexing large RDF data sets in secondary memory for efficient SPARQL evaluation

dc.creatorFletcher, George H. L.
dc.creatorBeck, Peter W.
dc.date2008-11-07
dc.date.accessioned2026-07-07T10:19:23Z
dc.date.available2026-07-07T10:19:23Z
dc.descriptionMassive RDF data sets are becoming commonplace. RDF data is typically generated in social semantic domains (such as personal information management) wherein a fixed schema is often not available a priori. We propose a simple Three-way Triple Tree (TripleT) secondary-memory indexing technique to facilitate efficient SPARQL query evaluation on such data sets. The novelty of TripleT is that (1) the index is built over the atoms occurring in the data set, rather than at a coarser granularity, such as whole triples occurring in the data set; and (2) the atoms are indexed regardless of the roles (i.e., subjects, predicates, or objects) they play in the triples of the data set. We show through extensive empirical evaluation that TripleT exhibits multiple orders of magnitude improvement over the state of the art on RDF indexing, in terms of both storage and query processing costs.
dc.description12 pages, 5 figures, 2 tables
dc.identifierhttps://arxiv.org/abs/0811.1083
dc.identifierhttp://arxiv.org/abs/0811.1083
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/174488
dc.subjectDatabases
dc.subjectData Structures and Algorithms
dc.titleA role-free approach to indexing large RDF data sets in secondary memory for efficient SPARQL evaluation
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