Advertisement

Real-Time Computation of Advanced Rules in OLAP Databases

  • Steffen Wittmer
  • Tobias Lauer
  • Amitava Datta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6909)

Abstract

In Online Analytical Processing (OLAP) users view data through a multidimensional model known as the data cube, allowing the aggregation of information along different attributes and operations such as slicing and dicing. In-memory OLAP systems keep all relevant data in main memory and also support efficient updates of cube data, enabling interactive planning, forecasting, and what-if analysis. Since usually only the base data is stored and all aggregations and other calculations are computed on the fly, complex computations may seriously downgrade performance. We present an approach that uses graphics processing units (GPUs) as parallel coprocessors for high performance in-memory OLAP operations. In particular, our method accelerates the calculation of compute-intensive rules, which represent business dependencies that are more complex than mere aggregates. In addition to the data structures and algorithms, we describe how to extend the approach to multi-GPU systems in order to scale it to larger data sets.

Keywords

Data Cube Fact Table Matching Fact Rule Operation Advance Rule 
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.
    Andrzejewski, W., Wrembel, R.: GPU-WAH: Applying GPUs to compressing bitmap Indexes with word aligned hybrid. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds.) DEXA 2010. LNCS, vol. 6262, pp. 315–329. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Bakkum, P., Skadron, K.: Accelerating SQL database operations on a GPU with CUDA. In: Proceedings of GPGPU 2010, pp. 94–103. ACM Press, New York (2010)Google Scholar
  3. 3.
    Chaudhuri, S., Dayal, U.: Data warehousing and OLAP for decision support. In: Proceedings of SIGMOD 1997, Tucson, AZ. ACM Press, New York (1997)Google Scholar
  4. 4.
  5. 5.
    Dehne, F., Eavis, T., Rau-Chaplin, A.: The cgmCUBE project: optimizing parallel data cube generation for ROLAP. Distributed and Parallel Databases 19(1), 29–62 (2006)CrossRefGoogle Scholar
  6. 6.
    Fernando, R.: GPU Gems: Programming Techniques, Tips and Tricks for Real-Time Graphics. Pearson Higher Education, London (2004)Google Scholar
  7. 7.
    Govindaraju, N.K., Lloyd, B., Wang, W., Lin, M.C., Manocha, D.: Fast computation of database operations using graphics processors. In: Proceedings of SIGMOD 2004, Paris, France, pp. 206–217. ACM Press, New York (June 2004)Google Scholar
  8. 8.
    Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., Pirahesh, H.: Data cube: a relational aggregation operator generalizing group-by, cross-tab, and sub-totals. Data Mining and Knowledge Discovery 1(1), 29–53 (1997)CrossRefGoogle Scholar
  9. 9.
    Harris, M., Sengupta, S., Owens, J.D.: Parallel Prefix Sum (Scan) with CUDA. In: Nguyen, H. (ed.) GPU Gems 3, pp. 851–876. Addison Wesley, Reading (August 2007)Google Scholar
  10. 10.
    He, B., Lu, M., Yang, K., Fang, R., Govindaraju, N.K., Luo, Q., Sander, P.V.: Relational query coprocessing on graphics processors. Transactions on Database Systems 34, 4 (2009)CrossRefGoogle Scholar
  11. 11.
    Horn, D.: Stream reduction operations for GPGPU applications. In: Pharr, M. (ed.) GPU Gems 2, pp. 573–589. Addison Wesley, Reading (March 2005)Google Scholar
  12. 12.
  13. 13.
  14. 14.
    Jedox Palo Suite, http://www.palo.net
  15. 15.
    Kaczmarski, K.: Comparing CPU and GPU in OLAP cube creation. In: Černá, I., Gyimóthy, T., Hromkovič, J., Jefferey, K., Králović, R., Vukolić, M., Wolf, S. (eds.) SOFSEM 2011. LNCS, vol. 6543, pp. 308–319. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    Lauer, T., Datta, A., Khadikov, Z., Anselm, C.: Exploring graphics processing units as parallel coprocessors for online aggregation. In: Proceedings of DOLAP 2010, Toronto, Canada. ACM Press, New York (October 2010)Google Scholar
  17. 17.
    Satish, N., Harris, M., Garland, M.: Designing efficient sorting algorithms for manycore GPUs. In: Proceedings of the IEEE International Symposium on Parallel & Distributed Processing, pp. 1–10 (May 2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Steffen Wittmer
    • 1
  • Tobias Lauer
    • 2
  • Amitava Datta
    • 3
  1. 1.Jedox AGFreiburgGermany
  2. 2.University of FreiburgGermany
  3. 3.University of Western AustraliaPerthAustralia

Personalised recommendations