Advertisement

Compact Querieable Representations of Raster Data

  • Guillermo de Bernardo
  • Sandra Álvarez-García
  • Nieves R. Brisaboa
  • Gonzalo Navarro
  • Oscar Pedreira
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8214)

Abstract

In Geographic Information Systems (GIS) the attributes of the space (altitude, temperature, etc.) are usually represented using a raster model. There are no compact representations of raster data that provide efficient query capabilities. In this paper we propose compact representations to efficiently store and query raster datasets in main memory. We experimentally compare our proposals with traditional storage mechanisms for raster data, showing that our structures obtain competitive space performance while efficiently answering range queries involving the values stored in the raster.

Keywords

Geographic Information System Geographic Information System Query Time Binary Matrix Raster Data 
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.
    Barbay, J., Gagie, T., Navarro, G., Nekrich, Y.: Alphabet partitioning for compressed rank/select and applications. In: Cheong, O., Chwa, K.-Y., Park, K. (eds.) ISAAC 2010, Part II. LNCS, vol. 6507, pp. 315–326. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Boldi, P., Vigna, S.: The Webgraph framework I: compression techniques. In: Proc. 13th WWW, pp. 595–602 (2004)Google Scholar
  3. 3.
    Brisaboa, N.R., de Bernardo, G., Navarro, G.: Compressed dynamic binary relations. In: Proc. 22nd DCC, pp. 52–61 (2012)Google Scholar
  4. 4.
    Brisaboa, N.R., Ladra, S., Navarro, G.: k2-Trees for compact web graph representation. In: Karlgren, J., Tarhio, J., Hyyrö, H. (eds.) SPIRE 2009. LNCS, vol. 5721, pp. 18–30. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Chan, T.M., Larsen, K.G., Pătraşcu, M.: Orthogonal range searching on the RAM, revisited. In: Proc. 27th SoCG, pp. 1–10 (2011)Google Scholar
  6. 6.
    Chang, H.K., Chang, J.W.: Fixed binary linear quadtree coding scheme for spatial data. In: Proc. 9th VCIP, vol. 2308, pp. 1214–1220 (1994)Google Scholar
  7. 7.
    Finkel, R.A., Bentley, J.L.: Quad trees: A data structure for retrieval on composite keys. Acta Informatica 4, 1–9 (1974)CrossRefzbMATHGoogle Scholar
  8. 8.
    Gargantini, I.: An effective way to represent quadtrees. Communications of the ACM 25(12), 905–910 (1982)CrossRefzbMATHGoogle Scholar
  9. 9.
    Golynski, A., Munro, J.I., Rao, S.S.: Rank/select operations on large alphabets: a tool for text indexing. In: Proc. 17th SODA, pp. 368–373 (2006)Google Scholar
  10. 10.
    Grossi, R., Gupta, A., Vitter, J.S.: High-order entropy-compressed text indexes. In: Proc. 14th SODA, pp. 841–850 (2003)Google Scholar
  11. 11.
    Lin, T.W.: Set operations on constant bit-length linear quadtrees. Pattern Recognition 30(7), 1239–1249 (1997)CrossRefGoogle Scholar
  12. 12.
    Munro, J.I.: Tables. In: Chandru, V., Vinay, V. (eds.) FSTTCS 1996. LNCS, vol. 1180, pp. 37–42. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  13. 13.
    Navarro, G.: Wavelet trees for all. In: Kärkkäinen, J., Stoye, J. (eds.) CPM 2012. LNCS, vol. 7354, pp. 2–26. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  14. 14.
    Rigaux, P., Scholl, M., Voisard, A.: Spatial databases - with applications to GIS. Elsevier (2002)Google Scholar
  15. 15.
    Welch, T.A.: A technique for high-performance data compression. Computer 17(6), 8–19 (1984)CrossRefGoogle Scholar
  16. 16.
    Worboys, M., Duckham, M.: GIS: A Computing Perspective, 2nd edn. CRC Press, Inc. (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Guillermo de Bernardo
    • 1
  • Sandra Álvarez-García
    • 1
  • Nieves R. Brisaboa
    • 1
  • Gonzalo Navarro
    • 2
  • Oscar Pedreira
    • 1
  1. 1.Databases Lab.University of A CoruñaSpain
  2. 2.Department of Computer ScienceUniversity of ChileChile

Personalised recommendations