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MapReduce Based Scalable Range Query Architecture for Big Spatial Data

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The Rise of Big Spatial Data

Part of the book series: Lecture Notes in Geoinformation and Cartography ((LNGC))

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Abstract

Finding all objects that overlap a given range query is very important in terms of extraction useful information from big spatial data. In this study, in order to be able to realize range query on large amounts of spatial data, three datasets are created with different size and a MapReduce computation model is set up to test scalability of range queries. Experimental results show that process times for range query reduce with increase of conventional machines.

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Acknowledgments

This work has been supported by the TUBITAK under Grant 215E189.

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Authors and Affiliations

  1. Department of Computer Engineering, Engineering Faculty, Kocaeli University, Umuttepe Campus, 41380, Izmit, Kocaeli, Turkey

    Süleyman Eken, Umut Kizgindere & Ahmet Sayar

Authors
  1. Süleyman Eken
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  2. Umut Kizgindere
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  3. Ahmet Sayar
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Corresponding author

Correspondence to Süleyman Eken .

Editor information

Editors and Affiliations

  1. Institute of Geoinformatics, VŠB-Technical University of Ostrava, Ostrava, Moravskoslezsky, Czech Republic

    Igor Ivan

  2. Department of Geography and Planning, University of Liverpool, Liverpool, Merseyside, United Kingdom

    Alex Singleton

  3. Institute of Geoinformatics, VŠB-Technical University of Ostrava, Ostrava, Moravskoslezsky, Czech Republic

    Jiří Horák

  4. Institute of Geoinformatics, VŠB-Technical University of Ostrava, Ostrava, Moravskoslezsky, Czech Republic

    Tomáš Inspektor

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Eken, S., Kizgindere, U., Sayar, A. (2017). MapReduce Based Scalable Range Query Architecture for Big Spatial Data. In: Ivan, I., Singleton, A., Horák, J., Inspektor, T. (eds) The Rise of Big Spatial Data. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-45123-7_19

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