HDTQ: Managing RDF Datasets in Compressed Space

Fernández, Javier D. and Martí­nez-Prieto, Miguel A. and Polleres, Axel ORCID: https://orcid.org/0000-0001-5670-1146 and Reindorf, Julian (2018) HDTQ: Managing RDF Datasets in Compressed Space. In: The Semantic Web. Proceedings of ESWC 2018. ESWC. Springer International Publishing, Cham. pp. 191-208.


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HDT (Header-Dictionary-Triples) is a compressed representation of RDF data that supports retrieval features without prior decompression. Yet, RDF datasets often contain additional graph information, such as the origin, version or validity time of a triple. Traditional HDT is not capable of handling this additional parameter(s). This work introduces HDTQ (HDT Quads), an extension of HDT that is able to represent quadruples (or quads) while still being highly compact and queryable. Two HDTQ-based approaches are introduced: Annotated Triples and Annotated Graphs, and their performance is compared to the leading open-source RDF stores on the market. Results show that HDTQ achieves the best compression rates and is a competitive alternative to well-established systems.

Item Type: Book Section
Additional Information: The final publication is available at Springer via https://doi.org/10.1007/978-3-319-93417-4_13. Supported by the EU's Horizon 2020 research and innovation programme: grant 731601 (SPECIAL), the Austrian Research Promotion Agency's (FFG) program "ICT of the Future": grant 861213 (CitySpin), and MINECO-AEI/FEDER-UE ETOME-RDFD3: TIN2015-69951-R and TIN2016-78011-C4-1-R, and the Distinguished Visiting Austrian Chair Professors program hosted by The Europe Center of Stanford University.
Divisions: Departments > Informationsverarbeitung u Prozessmanag. > Informationswirtschaft > Polleres
Version of the Document: Accepted for Publication
Variance from Published Version: Minor
Depositing User: Javier David Fernandez Garcia
Date Deposited: 06 Sep 2018 09:06
Last Modified: 24 Oct 2019 13:30
URI: https://epub.wu.ac.at/id/eprint/6482


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