HDTQ: Managing RDF Datasets in Compressed Space

Javier David Fernandez Garcia; Miguel A. Martínez-Prieto; Axel Polleres; Julian Reindorf

doi:10.1007/978-3-319-93417-4_13

HDTQ: Managing RDF Datasets in Compressed Space

Javier David Fernandez Garcia, Miguel A. Martínez-Prieto, Axel Polleres, Julian Reindorf

Institute for Data, Process and Knowledge Management (AE Polleres)

Publication: Chapter in book/Conference proceeding › Contribution to conference proceedings

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Abstract

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.

Original language	English
Title of host publication	The Semantic Web. Proceedings of ESWC 2018
Editors	Springer International Publishing
Place of Publication	Cham
Publisher	Springer International Publishing
Pages	191-208
ISBN (Print)	978-3-319-93417-4
DOIs	https://doi.org/10.1007/978-3-319-93417-4_13
Publication status	Published - 2018

Austrian Classification of Fields of Science and Technology (ÖFOS)

102001 Artificial intelligence
502050 Business informatics
102015 Information systems

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10.1007/978-3-319-93417-4_13Licence: Unspecified

HDTQAccepted author manuscript, 846 KBLicence: Other

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University Library Catalogue

Cyber-Physical Social Systems for City-wide Infrastructures
Cecconi, A. (Researcher), Di Ciccio, C. (Researcher), Fernandez Garcia, J. D. (Researcher), Mendling, J. (Researcher) & Polleres, A. (Researcher)
1/10/17 → 31/03/20
Project: Research funding
SPECIAL - Scalable Policy-awarE linked data arChitecture for prIvacy, trAnsparency and compLiance
Kirrane, S. (PI - Project head), Drozd, O. (Researcher), Fernandez Garcia, J. D. (Researcher), Havur, G. (Researcher), Polleres, A. (Researcher) & Spiekermann-Hoff, S. (Researcher)
1/01/17 → 31/12/19
Project: Research funding

Cite this

@inproceedings{e39408e9176a4d2492f55b3fd15596a7,

title = "HDTQ: Managing RDF Datasets in Compressed Space",

abstract = "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.",

author = "{Fernandez Garcia}, {Javier David} and Mart{\'i}nez-Prieto, {Miguel A.} and Axel Polleres and Julian Reindorf",

year = "2018",

doi = "10.1007/978-3-319-93417-4_13",

language = "English",

isbn = "978-3-319-93417-4",

pages = "191--208",

editor = "{Springer International Publishing}",

booktitle = "The Semantic Web. Proceedings of ESWC 2018",

publisher = "Springer International Publishing",

}

HDTQ: Managing RDF Datasets in Compressed Space. / Fernandez Garcia, Javier David; Martínez-Prieto, Miguel A.; Polleres, Axel et al.
The Semantic Web. Proceedings of ESWC 2018. ed. / Springer International Publishing. Cham: Springer International Publishing, 2018. p. 191-208.

Publication: Chapter in book/Conference proceeding › Contribution to conference proceedings

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T1 - HDTQ: Managing RDF Datasets in Compressed Space

AU - Fernandez Garcia, Javier David

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AU - Reindorf, Julian

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

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HDTQ: Managing RDF Datasets in Compressed Space

Abstract

Austrian Classification of Fields of Science and Technology (ÖFOS)

Access to Document

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Projects

Cyber-Physical Social Systems for City-wide Infrastructures

SPECIAL - Scalable Policy-awarE linked data arChitecture for prIvacy, trAnsparency and compLiance

Cite this