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Efficient RDF stream reasoning with graphics processingunits (GPUs)

Authors:

Chang Liu,

Jacopo Urbani,

Guilin QiAuthors Info & Claims

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

Pages 343 - 344

https://doi.org/10.1145/2567948.2577323

Published: 07 April 2014 Publication History

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Abstract

In this paper, we study the problem of stream reasoning and propose a reasoning approach over large amounts of RDF data, which uses graphics processing units (GPU) to improve the performance. First, we show how the problem of stream reasoning can be reduced to a temporal reasoning problem. Then, we describe a number of algorithms to perform stream reasoning with GPUs.

References

[1]

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M. Billeter, O. Olsson, and U. Assarsson. Efficient stream compaction on wide SIMD many-core architectures. In Proc. of HPG '09, 200

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N. Heino and J. Z. Pan. RDFS Reasoning on Massively Parallel Hardware. In Proceedings of ISWC, pages 133--148, 2012.

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S. Munoz, J. Perez, and C. Gutierrez. Minimal Deductive Systems for RDF. In Proceedings of ESWC, pages 53--67, 2007.

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E. D. Valle, S. Ceri, F. van Harmelen, and D. Fensel. It's a Streaming World! Reasoning upon Rapidly Changing Information. IEEE Intelligent Systems, 24(6):83--89, 2009.

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Cited By

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Chantrapornchai CMakpaisit P(2018)TripleID-CProceedings of the International Conference on High Performance Computing in Asia-Pacific Region10.1145/3149457.3155322(261-270)Online publication date: 28-Jan-2018
https://dl.acm.org/doi/10.1145/3149457.3155322
Xiȩski TNowak-Brzezińska A(2018)Different Methods for Cluster’s Representation and Their Impact on the Effectiveness of Searching Through Such a StructureComputational Collective Intelligence10.1007/978-3-319-98446-9_27(290-300)Online publication date: 8-Aug-2018
https://doi.org/10.1007/978-3-319-98446-9_27
Rao GZhao BZhang XFeng ZXiao G(2018)PRSPR: An Adaptive Framework for Massive RDF Stream ReasoningWeb and Big Data10.1007/978-3-319-96890-2_36(440-448)Online publication date: 19-Jul-2018
https://doi.org/10.1007/978-3-319-96890-2_36
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Index Terms

Efficient RDF stream reasoning with graphics processingunits (GPUs)
1. Information systems

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Information & Contributors

Information

Published In

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

April 2014

1396 pages

ISBN:9781450327459

DOI:10.1145/2567948

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
New York University, USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 April 2014

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Poster

Funding Sources

Dutch VENI project
Seventh Framework Programme

Conference

WWW '14

Sponsor:

IW3C2

WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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Chantrapornchai CMakpaisit P(2018)TripleID-CProceedings of the International Conference on High Performance Computing in Asia-Pacific Region10.1145/3149457.3155322(261-270)Online publication date: 28-Jan-2018
https://dl.acm.org/doi/10.1145/3149457.3155322
Xiȩski TNowak-Brzezińska A(2018)Different Methods for Cluster’s Representation and Their Impact on the Effectiveness of Searching Through Such a StructureComputational Collective Intelligence10.1007/978-3-319-98446-9_27(290-300)Online publication date: 8-Aug-2018
https://doi.org/10.1007/978-3-319-98446-9_27
Rao GZhao BZhang XFeng ZXiao G(2018)PRSPR: An Adaptive Framework for Massive RDF Stream ReasoningWeb and Big Data10.1007/978-3-319-96890-2_36(440-448)Online publication date: 19-Jul-2018
https://doi.org/10.1007/978-3-319-96890-2_36
Della Valle EDell'Aglio DMargara AGal AWeidlich MKalogeraki VVenkasubramanian N(2016)Taming velocity and variety simultaneously in big data with stream reasoningProceedings of the 10th ACM International Conference on Distributed and Event-based Systems10.1145/2933267.2933539(394-401)Online publication date: 13-Jun-2016
https://dl.acm.org/doi/10.1145/2933267.2933539
Chantrapornchai CChoksuchat CHaidl MGorlatch S(2016)TripleID: A Low-Overhead Representation and Querying Using GPU for Large RDFsBeyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery10.1007/978-3-319-34099-9_31(400-415)Online publication date: 28-Apr-2016
https://doi.org/10.1007/978-3-319-34099-9_31
Keeney JFallon LTai WO'Sullivan D(2015)Towards composite semantic reasoning for realtime network management data enrichmentProceedings of the 2015 11th International Conference on Network and Service Management (CNSM)10.1109/CNSM.2015.7367365(246-250)Online publication date: 9-Nov-2015
https://dl.acm.org/doi/10.1109/CNSM.2015.7367365

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