Advertisement

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)

Abstract

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.

Keywords

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baumann, P., Dehmel, A., Furtado, P., Ritsch, R., Widmann, N.: Spatio-temporal retrieval with RasDaMan. In: Proceedings of the 25th International Conference on Very Large Data Bases, VLDB 1999, pp. 746–749 (1999)Google Scholar
  2. 2.
    Campbell, P.: Editorial on special issue on big data: Community cleverness required. Nature 455(7209), 1 (2008)CrossRefGoogle Scholar
  3. 3.
    Cardelli, L., Wegner, P.: On understanding type, data abstraction, and polymorphism. ACM Computing Surveys 17(4), 471–552 (1985)CrossRefGoogle Scholar
  4. 4.
    Cordeiro, J., Camara, G., Freitas, U., Almeida, F.: Yet another map algebra. Geoinformatica 13(2), 183–202 (2009)CrossRefGoogle Scholar
  5. 5.
    Couclelis, H.: People manipulate objects (but cultivate fields): Beyond the raster-vector debate in GIS. In: Frank, A.U., Formentini, U., Campari, I. (eds.) GIS 1992. LNCS, vol. 639, pp. 65–77. Springer, Heidelberg (1992)CrossRefGoogle Scholar
  6. 6.
    Cudre-Mauroux, P., Kimura, H., Lim, K.T., Rogers, J., Madden, S., Stonebraker, M., Zdonik, S., Brown, P.: SS-DB: A standard science DBMS benchmark. In: XLDB 2010 - Extremely Large Databases Conference (2012)Google Scholar
  7. 7.
    Dean, J., Ghemawat, S.: MapReduce: Simplified data processing on large clusters. Communications ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  8. 8.
    Ferreira, K., Camara, G., Monteiro, A.: An algebra for spatiotemporal data: From observations to events. Transactions in GIS 18(2), 253–269 (2014)CrossRefGoogle Scholar
  9. 9.
    Frank, A.: One step up the abstraction ladder: Combining algebras - from functional pieces to a whole. In: Freksa, C., Mark, D.M. (eds.) COSIT 1999. LNCS, vol. 1661, pp. 95–108. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  10. 10.
    Frank, A.: Tiers of ontology and consistency constraints in geographic information systems. International Journal of Geographical Information Science 15(7), 667–678 (2001)CrossRefGoogle Scholar
  11. 11.
    Frank, A.: Map algebra extended with functors for temporal data. In: Akoka, J., et al. (eds.) ER Workshops 2005. LNCS, vol. 3770, pp. 194–207. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Frank, A.: GIS theory - the fundamental principles in GIScience: A mathematical approach. In: Harvey, F.J. (ed.) Are there Fundamental Principles in Geographic Information Science?, pp. 12–41 (2012)Google Scholar
  13. 13.
    Frank, A., Kuhn, W.: Specifying Open GIS with functional languages. In: Egenhofer, M., Herring, J.R. (eds.) SSD 1995. LNCS, vol. 951, pp. 184–195. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  14. 14.
    Galton, A.: Fields and objects in space, time and space-time. Spatial Cognition and Computation 4 (2004)Google Scholar
  15. 15.
    Goodchild, M.: Geographical data modeling. Computers and Geosciences 18(4), 401–408 (1992)CrossRefGoogle Scholar
  16. 16.
    Goodchild, M., Yuan, M., Cova, T.: Towards a general theory of geographic representation in GIS. International Journal of Geographical Information Science 21(3), 239–260 (2007)CrossRefGoogle Scholar
  17. 17.
    Guttag, J., Horowitz, E., Musser, D.: Abstract data types and software validation. Communications of the ACM 21(12), 1048–1064 (1978)CrossRefzbMATHMathSciNetGoogle Scholar
  18. 18.
    Hansen, M., Potapov, P., Moore, R., Hancher, M., Turubanova, S., Tyukavina, A., Thau, D., Stehman, S., Goetz, S., Loveland, T., Kommareddy, A., Egorov, A., Chini, L., Justice, C., Townshend, J.: High-resolution global maps of 21st-century forest cover change. Science 342(6160), 850–853 (2013)CrossRefGoogle Scholar
  19. 19.
    Jiang, Z., Huete, A., Didan, K., Miura, T.: Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment 112(10), 3833–3845 (2008)CrossRefGoogle Scholar
  20. 20.
    Justice, C., Townshend, J., Vermote, E., Masuoka, E., Wolfe, R., Saleous, N., Roy, D., Morisette, J.: An overview of MODIS land data processing and product status. Remote Sensing of Environment 83(1), 3–15 (2002)CrossRefGoogle Scholar
  21. 21.
    Kemp, K.: Fields as a framework for integrating GIS and environmental process models. part one: Representing spatial continuity. Transactions in GIS 1(3), 219–234 (1997)Google Scholar
  22. 22.
    Kuhn, W.: Geospatial semantics: Why, of what, and how? Journal of Data Semantics 3, 1–24 (2005)Google Scholar
  23. 23.
    Mennis, J.: Multidimensional map algebra: Design and implementation of a spatiotemporal GIS processing language. Transactions in GIS 14(1), 1–21 (2010)CrossRefGoogle Scholar
  24. 24.
    OGC: The OpenGIS abstract specification - Topic 6: Schema for coverage geometry and functions (Tech. Rep. OGC 07-011). Tech. rep., Open Geospatial Consortium, Inc. (2007)Google Scholar
  25. 25.
    OGC: OGC web coverage service (WCS) interface standard - Core (OGC 09-110r3). Tech. rep., Open Geospatial Consortium, Inc. (2010)Google Scholar
  26. 26.
    Peuquet, D.: Representations of geographic space: Toward a conceptual synthesis. Annals of the Association of American Geographers 78(3), 375–394 (1988)CrossRefGoogle Scholar
  27. 27.
    Planthaber, G., Stonebraker, M., Frew, J.: EarthDB: scalable analysis of MODIS data using SciDB. In: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, pp. 11–19. ACM (2012)Google Scholar
  28. 28.
    Sinton, D.: The inherent structure of information as a constraint to analysis: Mapped thematic data as a case study. In: Dutton, G. (ed.) Harvard Papers on Geographic Information Systems, vol. 7, pp. 1–17. Addison-Wesley, Reading (1978)Google Scholar
  29. 29.
    Stonebraker, M., Brown, P., Zhang, D., Becla, J.: SciDB: A database management system for applications with complex analytics. Computing in Science & Engineering 15(3), 54–62 (2013)CrossRefGoogle Scholar
  30. 30.
    Tomlin, C.: Geographic Information Systems and Cartographic Modeling. Prentice-Hall, Englewood Cliffs (1990)Google Scholar
  31. 31.
    Winter, S., Nittel, S.: Formal information modelling for standardisation in the spatial domain. International Journal of Geographical Information Science 17(8), 721–741 (2003)CrossRefGoogle Scholar

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

Personalised recommendations