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GeoWave: Utilizing Distributed Key-Value Stores for Multidimensional Data

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Advances in Spatial and Temporal Databases (SSTD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10411))

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Abstract

To date, it has been difficult for modern geospatial software projects to take advantage of the benefits provided by distributed computing frameworks due to the implicit challenges of spatial and spatiotemporal data. Chief among these issues is preserving locality between multidimensional objects and the single dimensional sort order imposed by key-value stores. We will use the open source framework GeoWave to harness the scalability of various distributed frameworks and integrate them with geospatial queries, analytics, and map rendering. GeoWave performs dimensionality reduction by utilizing space–filling curves to convert n-dimensional data into a single dimension. This ensures that values close in multidimensional space are highly contiguous in the single dimensional keys of the datastore. By using various forms of geospatial data, we show that preserving locality in this way reduces the time needed to query, analyze, and render large amounts of data by multiple orders of magnitude.

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Author information

Authors and Affiliations

  1. DigitalGlobe, Herndon, VA, USA

    Michael A. Whitby, Rich Fecher & Chris Bennight

Authors
  1. Michael A. Whitby
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  2. Rich Fecher
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  3. Chris Bennight
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Corresponding author

Correspondence to Michael A. Whitby .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, Heidelberg University, Heidelberg, Germany

    Michael Gertz

  2. George Mason University, Fairfax, Virginia, USA

    Matthias Renz

  3. University of Queensland, Brisbane, Queensland, Australia

    Xiaofang Zhou

  4. ESRI, University of Minnesota, Minneapolis, Minnesota, USA

    Erik Hoel

  5. Auburn University, Auburn, Alabama, USA

    Wei-Shinn Ku

  6. Free University of Berlin, Dahlem, Berlin, Germany

    Agnes Voisard

  7. Microsoft, Redmond, Washington, USA

    Chengyang Zhang

  8. California State University, Sacramento, California, USA

    Haiquan Chen

  9. LinkedIn, Sunnyvale, California, USA

    Liang Tang

  10. University of North Texas, Denton, Texas, USA

    Yan Huang

  11. Virginia Tech, Falls Church, Virginia, USA

    Chang-Tien Lu

  12. Oracle, Redwood Shores, California, USA

    Siva Ravada

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Whitby, M.A., Fecher, R., Bennight, C. (2017). GeoWave: Utilizing Distributed Key-Value Stores for Multidimensional Data. In: Gertz, M., et al. Advances in Spatial and Temporal Databases. SSTD 2017. Lecture Notes in Computer Science(), vol 10411. Springer, Cham. https://doi.org/10.1007/978-3-319-64367-0_6

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  • DOI: https://doi.org/10.1007/978-3-319-64367-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64366-3

  • Online ISBN: 978-3-319-64367-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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