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Pattern Matching Over Linked Data Streams

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Handbook of Big Data Technologies

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Abstract

This chapter leverages semantic technologies, such as Linked Data, which can facilitate machine-to-machine (M2M) communications to build an efficient information dissemination system for semantic IoT. The system integrates Linked Data streams generated from various data collectors and disseminates matched data to relevant data consumers based on triple pattern queries registered in the system by the consumers. We also design two new data structures, TP-automata and CTP-automata, to meet the high performance needs of Linked Data dissemination. We evaluate our system using a real-world dataset generated from a Smart Building Project. With the new data structures, the proposed system can disseminate Linked Data faster than the existing approach with thousands of registered queries.

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Notes

  1. 1.

    http://www-01.ibm.com/software/data/bigdata/.

  2. 2.

    There are many different hash functions that are suitable for this purpose. For more details, please refer to [9].

  3. 3.

    The source code and documentation of CQELS can be obtained via http://code.google.com/p/cqels/.

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Acknowledgements

We would like to thank the following researchers for their insightful feedback on our work: Edward Curry, Nickolas J.G. Falkner, and Ali Shemshadi.

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Authors and Affiliations

  1. School of Computing and Engineering, University of Huddersfield, Huddersfield, HD1 3DH, UK

    Yongrui Qin

  2. School of Computer Science, The University of Adelaide, Adelaide, SA, 5005, Australia

    Quan Z. Sheng

Authors
  1. Yongrui Qin
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  2. Quan Z. Sheng
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Correspondence to Yongrui Qin .

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Editors and Affiliations

  1. School of Information Technologies, The University of Sydney, Sydney, New South Wales, Australia

    Albert Y. Zomaya

  2. The School of Computer Science, The University of New South Wales, Eveleigh, New South Wales, Australia

    Sherif Sakr

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Qin, Y., Sheng, Q.Z. (2017). Pattern Matching Over Linked Data Streams. In: Zomaya, A., Sakr, S. (eds) Handbook of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-49340-4_12

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  • DOI: https://doi.org/10.1007/978-3-319-49340-4_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49339-8

  • Online ISBN: 978-3-319-49340-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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