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

TitleTorpedo: Improving the State-of-the-Art RDF Dataset Slicing
Publication TypeConference Paper
Year of Publication2017
AuthorsEdgard Marx, Saeedeh Shekarpour, Tommaso Soru, Adrian Brasoveanu, Muhammad Saleem, Ciro Baron, Albert Weichselbraun, Jens Lehmann, Axel-Cyrille Ngonga Ngomo, Sören Auer
Conference Name11th International Conference on Semantic Computing (IEEE ICSC 2017)
Conference LocationSan Diego, California
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.

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Citation Format:
E. Marx, S. Shekarpour, T. Soru, A. Brasoveanu, M. Saleem, C. Baron, A. Weichselbraun, J. Lehmann, A. N. Ngomo, S. Auer (2017). Torpedo: Improving the State-of-the-Art RDF Dataset Slicing. 11th International Conference on Semantic Computing (IEEE ICSC 2017), San Diego, California, Jan 30 - Feb 1, 2017.

Note: This paper received an honorable mention in the conference as one of the top 6 papers.

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