Efficient Map Portrayal Using a General-Purpose Query Language

(A Case Study)
  • Peter Baumann
  • Constantin Jucovschi
  • Sorin Stancu-Mara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5690)


Fast image generation from vector or raster data for map navigation by Web clients is an important geo Web application today. Raster data obviously account for the larger volume of the underlying data sets served through WMS and other such interfaces. Dedicated server implementations prevail because an often heard argument is that general-purpose server software, such as database systems, cannot be efficient enough for such high-volume application scenarios.

In this paper we refute that. We investigate just-in-time compilation of query fragments in two variants, for CPU and GPU, as implemented in the general purpose raster DBMS rasdaman. Results suggest that array databases are suitable for realtime geo raster services.


Graphic Processing Unit Query Processing Query Evaluation Raster Data Query Tree 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Acheson, A., et al.: Hosting runtime in microsoft sql server. In: Proc. ACM SIGMOD, pp. 860–865. ACM, New York (2004)Google Scholar
  2. 2.
    Baumann, P.: On the management of multi-dimensional discrete data. VLDB Journal Special Issue on Spatial Database Systems 4(3), 401–444 (1994)Google Scholar
  3. 3.
    Baumann, P.: Large-scale raster services: A case for databases (invited keynote). In: Roddick, J., Benjamins, V.R., Si-said Cherfi, S., Chiang, R., Claramunt, C., Elmasri, R.A., Grandi, F., Han, H., Hepp, M., Lytras, M.D., Mišić, V.B., Poels, G., Song, I.-Y., Trujillo, J., Vangenot, C. (eds.) ER Workshops 2006. LNCS, vol. 4231, pp. 75–84. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Baumann, P.: The ogc web coverage processing service (wcps) standard. Geoinformatica (2009) (accepted for publication)Google Scholar
  5. 5.
    Baumann, P.: A database array algebra for spatio-temporal data and beyond. In: Tsur, S. (ed.) NGITS 1999. LNCS, vol. 1649, pp. 76–93. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  6. 6.
    Furtado, P., Baumann, P.: Storage of multidimensional arrays based on arbitrary tiling. In: Proc. ICDE, pp. 328–336 (1999)Google Scholar
  7. 7.
    Gao, G.R., Olsen, R., Sarkar, V., Thekkathdw, R.: Collective loop fusion for array contraction. In: Banerjee, U., Gelernter, D., Nicolau, A., Padua, D.A. (eds.) LCPC 1992. LNCS, vol. 757, pp. 281–295. Springer, Heidelberg (1993)CrossRefGoogle Scholar
  8. 8.
    Graefe, G.: Query evaluation techniques for large databases. ACM Comput. Surv. 25(2), 73–169 (1993)CrossRefGoogle Scholar
  9. 9.
    Gutierrez, A.G.: The Application of OLAP Pre-Aggregation Techniques to Speed Up Query Processing in Raster-Image Databases. Phd thesis (2009)Google Scholar
  10. 10.
    Gutierrez, A.G., Baumann, P.: Computing aggregate queries in raster image databases using pre-aggregated data. In: Proc. ICCSA (2008)Google Scholar
  11. 11.
    Hahn, K., Reiner, B., Höfling, G., Baumann, P.: Parallel query support for multidimensional data: Inter-object parallelism. In: Hameurlain, A., Cicchetti, R., Traunmüller, R. (eds.) DEXA 2002. LNCS, vol. 2453, p. 820. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Jucovschi, C.: Precompiling Queries in a Raster Database System. Bachelor thesis, Jacobs University Bremen (2008)Google Scholar
  13. 13.
    Jucovschi, C., Baumann, P., Stancu-Mara, S.: Speeding up array query processing by just-in-time compilation. In: Proc. IEEE SSTDM, pp. 408–413 (2008)Google Scholar
  14. 14.
    Libkin, L., Machlin, R., Wong, L.: A query language for multidimensional arrays: design, implementation and optimization techniques. In: ACM SIGMOD, pp. 228–239 (1996)Google Scholar
  15. 15.
    Marathe, A.P., Salem, K.: Query processing techniques for arrays. VLDB Journal 11(1), 68–91 (2002)CrossRefGoogle Scholar
  16. 16.
    n. n. Ecw – ermapper compress wavelets (.ecw), (accessed June 13, 2009)
  17. 17.
    n. n. Jpeg2000, (accessed June 13, 2009)
  18. 18.
    Neugebauer, L.: Optimization and evaluation of database queries including embedded interpolation procedures. SIGMOD Rec. 20(2), 118–127 (1991)CrossRefGoogle Scholar
  19. 19.
    n.n. Mrsid – multi-resolution seamless image database, (accessed June 13, 2009)
  20. 20.
    n.n. rasdaman query language guide, 7.0 ed. rasdaman GmbH (2008)Google Scholar
  21. 21.
    Pisarev, A., Poustelnikova, E., Samsonova, M., Baumann, P.: Mooshka: a system for the management of multidimensional gene expression data in situ. Information Systems 28, 269–285 (2003)CrossRefzbMATHGoogle Scholar
  22. 22.
    Ritsch, R.: Optimization and Evaluation of Array Queries in Database Management Systems. Phd thesis (1999)Google Scholar
  23. 23.
    Roland, P., Svensson, G., Lindeberg, T., Risch, T., Baumann, P., Dehmel, A., Frederiksson, J., Halldorson, H., Forsberg, L., Young, J., Zilles, K.: A database generator for human brain imaging. Trends in Neurosciences 24(10), 562–564 (2001)CrossRefGoogle Scholar
  24. 24.
    Rost, R.J.: OpenGL shading language. Addison-Wesley, Reading (2006)Google Scholar
  25. 25.
    Stancu-Mara, S.: Method for server-side data processing using graphic processing units (2007)Google Scholar
  26. 26.
    Stancu-Mara, S.: Using Graphic Cards for Accelerating rater Database Query Processing. Bachelor thesis, Jacobs University Bremen (2008)Google Scholar
  27. 27.
    Stancu-Mara, S.: Optimization Support for Linear Indexed Queries in Raster Databases. Master thesis, Jacobs University Bremen (2009)Google Scholar
  28. 28.
    Trissl, S., Leser, U.: Fast and practical indexing and querying of very large graphs. In: Proc. ACM SIGMOD, pp. 845–856. ACM, New York (2007)Google Scholar
  29. 29.
    van Ballegooij, A.R.: RAM: A multidimensional array DBMS. In: Lindner, W., Mesiti, M., Türker, C., Tzitzikas, Y., Vakali, A.I. (eds.) EDBT 2004. LNCS, vol. 3268, pp. 154–165. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  30. 30.
    Widmann, N.: Efficient Operation Execution on Multidimensional Array Data. Phd thesis (2000)Google Scholar
  31. 31.
    Widmann, N., Baumann, P.: Efficient execution of operations in a DBMS for multidimensional arrays. In: Proc. SSDBM, pp. 155–165 (1998)Google Scholar
  32. 32.
    Widmann, N., Baumann, P.: Performance evaluation of multidimensional array storage techniques in databases. In: Proc. IDEAS (1999)Google Scholar
  33. 33.
    Wiedmann, C.: A performance comparison between an apl interpreter and compiler. In: Proc. APL, pp. 211–217. ACM, New York (1983)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Peter Baumann
    • 1
  • Constantin Jucovschi
    • 1
  • Sorin Stancu-Mara
    • 1
  1. 1.Jacobs University BremenGermany

Personalised recommendations