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A Hadoop Extension for Analysing Spatiotemporally Referenced Events

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Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017 (AISI 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 639))

Abstract

A spatiotemporally referenced event is a tuple that contains both a spatial reference and a temporal reference. The spatial reference is typically a point coordinate, and the temporal reference is a timestamp. The event payload can be the reading of a sensor (IoT systems), a user comment (geo-tagged social networks), a news article (gdelt), etc. Spatiotemporal event datasets are ever growing, and the requirements for their processing goes beyond traditional client-sever GIS architectures. Rather, Hadoop-like architectures shall be used. Yet, Hadoop does not provide the types and operations necessary for processing such datasets. In this paper, we propose a Hadoop extension (indeed a SpatialHadoop extension) capable of performing analytics on big spatiotemporally referenced event dataset. The extension includes data types and operators that are integrated into the Hadoop core, to be used as natives. We further optimize the querying by means of a spatiotemporal index. Experiments on the gdelt event dataset demonstrate the utility of the proposed extension.

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Author information

Authors and Affiliations

  1. Faculty of Computers and Information, Assiut University, Asyut, Egypt

    Mohamed S. Bakli & Taysir Hassan A. Soliman

  2. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Mahmoud A. Sakr

  3. Universit Libre de Bruxelles, Bruxelles, Belgium

    Mahmoud A. Sakr

Authors
  1. Mohamed S. Bakli
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  2. Mahmoud A. Sakr
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  3. Taysir Hassan A. Soliman
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Corresponding author

Correspondence to Mohamed S. Bakli .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Information Technology Department, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Dubai International Academic City, The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

  3. Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  4. Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

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Bakli, M.S., Sakr, M.A., Soliman, T.H.A. (2018). A Hadoop Extension for Analysing Spatiotemporally Referenced Events. In: Hassanien, A., Shaalan, K., Gaber, T., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017. AISI 2017. Advances in Intelligent Systems and Computing, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-64861-3_85

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  • DOI: https://doi.org/10.1007/978-3-319-64861-3_85

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64860-6

  • Online ISBN: 978-3-319-64861-3

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