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International Conference: Beyond Databases, Architectures and Structures

International Conference: Beyond Databases, Architectures and Structures

BDAS 2015, BDAS 2016: Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery pp 400–415Cite as

TripleID: A Low-Overhead Representation and Querying Using GPU for Large RDFs

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 613))

Abstract

Resource Description Framework (RDF) is a commonly used format for semantic web processing. It basically contains strings representing terms and their relationships which can be queried or inferred. RDF is usually a large text file which contains many million relationships. In this work, we propose a framework, TripleID, for processing queries of large RDF data. The framework utilises Graphics Processing Units (GPUs) to search RDF relations. The RDF data is first transformed to the encoded form suitable for storing in the GPU memory. Then parallel threads on the GPU search the required data. We show in the experiments that one GPU on a personal desktop can handle 100 million triple relations, while a traditional RDF processing tool can process up to 10 million triples. Furthermore, we can query sample relations within 0.18 s with the GPU in 7 million triples, while the traditional tool takes at least 6 s for 1.8 million triples.

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Acknowledgement

This work was supported in part by the following institutes and research programs: The Thailand Research Fund (TRF) through the Royal Golden Jubilee Ph.D. Program under Grant PHD/0005/2554, DAAD (German Academic Exchange Service) Scholarship project id: 57084841, NVIDIA Hardware grant, and the Faculty of Engineering at Kasetsart University Research funding contract no. 57/12/MATE.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Kasetsart University, Bangkok, Thailand

    Chantana Chantrapornchai

  2. Department of Computing, Silpakorn University, Bangkok, Thailand

    Chidchanok Choksuchat

  3. University of Münster, Münster, Germany

    Michael Haidl & Sergei Gorlatch

Authors
  1. Chantana Chantrapornchai
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  2. Chidchanok Choksuchat
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  3. Michael Haidl
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  4. Sergei Gorlatch
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Corresponding author

Correspondence to Chantana Chantrapornchai .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Dariusz Mrozek

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Paweł Kasprowski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Bożena Małysiak-Mrozek

  5. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Daniel Kostrzewa

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© 2016 Springer International Publishing Switzerland

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Cite this paper

Chantrapornchai, C., Choksuchat, C., Haidl, M., Gorlatch, S. (2016). TripleID: A Low-Overhead Representation and Querying Using GPU for Large RDFs. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery. BDAS BDAS 2015 2016. Communications in Computer and Information Science, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-34099-9_31

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  • DOI: https://doi.org/10.1007/978-3-319-34099-9_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-34098-2

  • Online ISBN: 978-3-319-34099-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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