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Torpedo: Improving the State-of-the-Art RDF Dataset Slicing

Marx, Edgard and Shekarpour, Saeedeh and Soru, Tomasso and Brasoveanu, Adrian M. P. and Muhammad, Saleem and Baron, Ciro and Weichselbraun, Albert and Lehmann, Jens and Ngonga, Axel-Cyrille and Auer, Soren (2017) Torpedo: Improving the State-of-the-Art RDF Dataset Slicing. In: IEEE ICSC 2017, Jan 30 - Feb 1, 2017, San Diego, California, USA.

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Official URL: http://ieeexplore.ieee.org/document/7889522/?reloa...

Abstract

Over the last years, the amount of data published as Linked Data on the Web has grown enormously. In spite of the high availability of Linked Data, organizations still encounter an accessibility challenge while consuming it. This is mostly due to the large size of some of the datasets published as Linked Data. The core observation behind this work is that a subset of these datasets suffices to address the needs of most organizations. In this paper, we introduce Torpedo, an approach for efficiently selecting and extracting relevant subsets from RDF datasets. In particular, Torpedo adds optimization techniques to reduce seek operations costs as well as the support of multi-join graph patterns and SPARQL FILTERs that enable to perform a more granular data selection. We compare the performance of our approach with existing solutions on nine different queries against four datasets. Our results show that our approach is highly scalable and is up to 26% faster than the current state-of-the-art RDF dataset slicing approach.

Item Type:Conference or Workshop Item (Paper)
Subjects:Q Science > QA Mathematics > QA76 Computer software
ID Code:107
Deposited By: Adrian
Deposited On:27 Oct 2017 08:06
Last Modified:27 Oct 2017 08:06

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