Scalable indexing of RDF graphs for efficient join processing

G.H.L. Fletcher, P.W. Beck

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

36 Citations (Scopus)


Current approaches to RDF graph indexing suffer from weak data locality, i.e., information regarding a piece of data appears in multiple locations, spanning multiple data structures. Weak data locality negatively impacts storage and query processing costs. Towards stronger data locality, we propose a Three-way Triple Tree (TripleT) secondary memory indexing technique to facilitate flexible and efficient join evaluation on RDF data. The novelty of TripleT is that 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, 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, in terms of both storage and query processing costs.
Original languageEnglish
Title of host publicationProceedings 18th ACM Conference on Information and Knowledge Management (CIKM2009, Hong Kong, November 2-6, 2009)
EditorsD.W.-L. Cheung, I.-Y. Song, W. Chu, X. Hu, J. Lin
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
ISBN (Print)978-1-60558-512-3
Publication statusPublished - 2009


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