A triple store implementation to support tabular data

Neil Brittliff, Dharmendra Sharma

Research output: A Conference proceeding or a Chapter in BookConference contribution

Abstract

The acceptance of Triple Stores and the adoption of Triple Store as an alternative to relational database technology has not met the expectations demanded by the Big Data community. In addition, most triple store implementations are unable to store and extract tabular data as fast as many of the alternate Big Data solutions that are currently available. What is missing is a Triple Store implementation that can contain both graphical and tabular data. The SPARQL property path extension which was introduced into the SPARQL 1.1 specification provides the capability to retrieve tabular data through the traversal of unbounded RDF list structures. However, there are no currently available Triple Store implementations that fully support the SPARQL 1.1 property path extension and therefore are unable to traverse large RDF lists using the SPARQL language. This paper will describe a triple store implementation project targeting the property path extension utilizing features found in most columnar storage databases.

Original languageEnglish
Title of host publicationData Mining and Analytics 2014
Subtitle of host publicationProceedings of the 12th Australasian Data Mining Conference (AusDM 2014)
EditorsXue Li, Lin Liu, Kok-Leong Ong, Yanchang Zhao
PublisherAustralian Computer Society
Pages59-67
Number of pages9
Volume158
ISBN (Print)9781921770173
Publication statusPublished - 2014
EventTwelfth Australasian Data Mining Conference - Brisbane, Brisbane, Australia
Duration: 27 Nov 201428 Nov 2014
http://ausdm14.ausdm.org/

Publication series

NameConferences in Research and Practice in Information Technology Series
Volume158
ISSN (Print)1445-1336

Conference

ConferenceTwelfth Australasian Data Mining Conference
Abbreviated titleAusDM14
CountryAustralia
CityBrisbane
Period27/11/1428/11/14
Internet address

Fingerprint

Specifications
Big data

Cite this

Brittliff, N., & Sharma, D. (2014). A triple store implementation to support tabular data. In X. Li, L. Liu, K-L. Ong, & Y. Zhao (Eds.), Data Mining and Analytics 2014: Proceedings of the 12th Australasian Data Mining Conference (AusDM 2014) (Vol. 158, pp. 59-67). (Conferences in Research and Practice in Information Technology Series; Vol. 158). Australian Computer Society.
Brittliff, Neil ; Sharma, Dharmendra. / A triple store implementation to support tabular data. Data Mining and Analytics 2014: Proceedings of the 12th Australasian Data Mining Conference (AusDM 2014). editor / Xue Li ; Lin Liu ; Kok-Leong Ong ; Yanchang Zhao. Vol. 158 Australian Computer Society, 2014. pp. 59-67 (Conferences in Research and Practice in Information Technology Series).
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abstract = "The acceptance of Triple Stores and the adoption of Triple Store as an alternative to relational database technology has not met the expectations demanded by the Big Data community. In addition, most triple store implementations are unable to store and extract tabular data as fast as many of the alternate Big Data solutions that are currently available. What is missing is a Triple Store implementation that can contain both graphical and tabular data. The SPARQL property path extension which was introduced into the SPARQL 1.1 specification provides the capability to retrieve tabular data through the traversal of unbounded RDF list structures. However, there are no currently available Triple Store implementations that fully support the SPARQL 1.1 property path extension and therefore are unable to traverse large RDF lists using the SPARQL language. This paper will describe a triple store implementation project targeting the property path extension utilizing features found in most columnar storage databases.",
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Brittliff, N & Sharma, D 2014, A triple store implementation to support tabular data. in X Li, L Liu, K-L Ong & Y Zhao (eds), Data Mining and Analytics 2014: Proceedings of the 12th Australasian Data Mining Conference (AusDM 2014). vol. 158, Conferences in Research and Practice in Information Technology Series, vol. 158, Australian Computer Society, pp. 59-67, Twelfth Australasian Data Mining Conference, Brisbane, Australia, 27/11/14.

A triple store implementation to support tabular data. / Brittliff, Neil; Sharma, Dharmendra.

Data Mining and Analytics 2014: Proceedings of the 12th Australasian Data Mining Conference (AusDM 2014). ed. / Xue Li; Lin Liu; Kok-Leong Ong; Yanchang Zhao. Vol. 158 Australian Computer Society, 2014. p. 59-67 (Conferences in Research and Practice in Information Technology Series; Vol. 158).

Research output: A Conference proceeding or a Chapter in BookConference contribution

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Brittliff N, Sharma D. A triple store implementation to support tabular data. In Li X, Liu L, Ong K-L, Zhao Y, editors, Data Mining and Analytics 2014: Proceedings of the 12th Australasian Data Mining Conference (AusDM 2014). Vol. 158. Australian Computer Society. 2014. p. 59-67. (Conferences in Research and Practice in Information Technology Series).