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Enabling Standard Geospatial Capabilities in Spark for the Efficient Processing of Geospatial Big Data

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Geographical Information Systems Theory, Applications and Management (GISTAM 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1061))

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Abstract

Nowadays, big data are in the midst of many scientific, economic and societal issues. While most of these data include a spatial component, very few big data processing systems are able to manage this particular component. The authors have assessed the capabilities and limits of current solutions and have concluded that most of them are neither efficient nor extensive enough for spatial data. Furthermore, none of them fully complies with ISO standards and OGC specifications in terms of spatial processing. The authors have sought a way to overcome these limitations and have defined a system in greater accordance with the ISO-19125 standard. The proposed solution, called Elcano, is an extension of Spark complying with ISO-19125, allowing the SQL querying of spatial data and including an original spatial indexation system. Tests demonstrate that Elcano surpasses current available solutions on the market.

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Acknowledgements

We acknowledge the support of the Natural Sciences and Engineering Council of Cana-da (NSERC), funding reference number 327533. We also thank Université Laval and especially the Center for Research in Geomatics (CRG) and the Faculty of Forestry, Geography and Geomatics for their support and their funding. Thanks to, Eveline Bernier, Cecilia Inverardi and Pierrot Seban for their thorough proof reading in the writing of this paper.

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Authors and Affiliations

  1. Centre for Research in Geomatics (CRG), Laval University, Quebec, Canada

    Jonathan Engélinus, Thierry Badard & Éveline Bernier

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  1. Jonathan Engélinus
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  2. Thierry Badard
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  3. Éveline Bernier
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Corresponding author

Correspondence to Thierry Badard .

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Editors and Affiliations

  1. Technical University of Crete, Chania, Crete, Greece

    Lemonia Ragia

  2. Polytechnic Institute of Setúbal, Setúbal, Portugal

    Cédric Grueau

  3. Knowledge Systems Institute, Skokie, IL, USA

    Robert Laurini

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Engélinus, J., Badard, T., Bernier, É. (2019). Enabling Standard Geospatial Capabilities in Spark for the Efficient Processing of Geospatial Big Data. In: Ragia, L., Grueau, C., Laurini, R. (eds) Geographical Information Systems Theory, Applications and Management. GISTAM 2018. Communications in Computer and Information Science, vol 1061. Springer, Cham. https://doi.org/10.1007/978-3-030-29948-4_7

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  • DOI: https://doi.org/10.1007/978-3-030-29948-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29947-7

  • Online ISBN: 978-3-030-29948-4

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