Fields as a Generic Data Type for Big Spatial Data

  • Gilberto Camara
  • Max J. Egenhofer
  • Karine Ferreira
  • Pedro Andrade
  • Gilberto Queiroz
  • Alber Sanchez
  • Jim Jones
  • Lubia Vinhas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8728)


This paper defines the Field data type for big spatial data. Most big spatial data sets provide information about properties of reality in continuous way, which leads to their representation as fields. We develop a generic data type for fields that can represent different types of spatiotemporal data, such as trajectories, time series, remote sensing and, climate data. To assess its power of generality, we show how to represent existing algebras for spatial data with the Fields data type. The paper also argues that array databases are the best support for processing big spatial data and shows how to use the Fields data type with array databases.


Spatial Data Enhance Vegetation Index Spatiotemporal Data Open Geospatial Consortium Forest Cover Change 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Gilberto Camara
    • 1
    • 2
  • Max J. Egenhofer
    • 3
  • Karine Ferreira
    • 1
  • Pedro Andrade
    • 1
  • Gilberto Queiroz
    • 1
  • Alber Sanchez
    • 2
  • Jim Jones
    • 2
  • Lubia Vinhas
    • 1
  1. 1.Image Processing DivisionNational Institute for Space Research (INPE)São José dos CamposBrazil
  2. 2.Institute for Geoinformatics (ifgi)University of MünsterGermany
  3. 3.National Center for Geographic Information and Analysis and School of Computing and Information ScienceUniversity of MaineOronoUSA

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