The Community for Technology Leaders
2017 IEEE 11th International Conference on Semantic Computing (ICSC) (2017)
San Diego, California, USA
Jan. 30, 2017 to Feb. 1, 2017
ISBN: 978-1-5090-4284-5
pp: 149-156
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.
INDEX TERMS
RDF dataset slicing, Relevant Fragment Extraction, Slicing distributed open dataset,
CITATION
Edgard Marx, Saeedeh Shekarpour, Tommaso Soru, Adrian M. P. Brasoveanu, Muhammad Saleem, Ciro Baron, Albert Weichselbraun, Jens Lehmann, Axel-Cyrille Ngonga Ngomo, Soren Auer, "Torpedo: Improving the State-of-the-Art RDF Dataset Slicing", 2017 IEEE 11th International Conference on Semantic Computing (ICSC), vol. 00, no. , pp. 149-156, 2017, doi:10.1109/ICSC.2017.79
92 ms
(Ver 3.3 (11022016))