Skip to main content

Query Processing in Data Warehouses

  • Reference work entry
Encyclopedia of Database Systems

Synonyms

Data warehouse query processing; Query answering in analytical domains; Query optimization for multidimensional systems; Query execution in star/snowflake schemas

Definition

Data warehouses usually store a tremendous amount of data, which is advantageous and yet challenging at the same time, since the particular querying/updating/modeling characteristics make query processing rather difficult due to the high number of degrees of freedom.

Typical data warehouse queries are usually generated by on-line analytical processing (OLAP) or data miningsoftware components. They show an extremely complex structure and usually address a large number of rows of the underlying database. For example, consider the following query: ‘Compute the monthly variation in the behavior of seasonal sales for all European countries but restrict the calculations to stores with > 1 million turnover in the same period of the last year and incorporate only 10% of all products with more than 8% market...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 2,500.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Recommended Reading

  1. Celko J. Joe Celko’s Data Warehouse and Analytic Queries in SQL. Morgan Kaufmann, 2006.

    Google Scholar 

  2. Chan C.-Y. 1998.Bitmap index design and evaluation. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pp. 355–366.

    Google Scholar 

  3. Chaudhuri S.and Dayal U. 1997.An Overview of Data Warehousing and OLAP Technology. ACM SIGMOD Rec., 26(1):65–74,

    Google Scholar 

  4. Clement T.Y. and Meng W. Principles of Database Query Processing for Advanced Applications. Morgan Kaufmann, 1997.

    Google Scholar 

  5. Data Mining Extensions (DMX) Reference. Available at: http://msdn2.microsoft.com/en-us/library/ms132058.aspx

  6. Graefe G. 1993, Query Evaluation Techniques for Large Databases. In ACM Comput. Surv., 25(2), S. 73–170.

    Google Scholar 

  7. Gray J. et al. The Lowell Database Research Self Assessment, June 2003. Available at: http://research.microsoft.com/∼gray/lowell/

  8. Gupta A. and Mumick I. Materialized Views: Techniques, Implementations and Applications. MIT Press, Cambridge, MA, 1999.

    Google Scholar 

  9. Hellerstein J.M., Haas P.J., and Wang H.J. Online Aggregation. In Proc. ACM SIGMOD Int. Conf. on Management of Data,1997, pp. 171–182.

    Google Scholar 

  10. Inmon W.H. Building the Data Warehouse. 2nd edn, Wiley, NY, USA.

    Google Scholar 

  11. Niemiec R. Oracle Database 10g Performance Tuning Tips & Techniques, 2007.

    Google Scholar 

  12. N.N. ISO/IEC 9075–14:2003: Information technology – Database languages – SQL – Part 14: XML-Related Specifications (SQL/XML). Available at: http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail.htm?csnumber = 35341.

  13. N.N. Multidimensional Expressions (MDX) Reference. Available at: http://msdn2.microsoft.com/en-us/library/ms145506.aspx.

  14. Roussopoulos N. 1982.The logical access path schema of a database. In IEEE Trans. Softw. Eng., 8, (6)S.563–573,

    MathSciNet  Google Scholar 

  15. Tao Y., Zhu Q., Zuzarte C., and Lau W. Optimizing large star-schema queries with snowflakes via heuristic-based query rewriting. In Proc. Conf. of the IBM Centre for Advanced Studies on Collaborative Research, 2003, pp. 279–293.

    Google Scholar 

  16. Valduriez P. 1987.Join indices. ACM Trans. Database Syst., 12(2):218–246,

    Google Scholar 

  17. Weininger A. Efficient execution of joins in a star schema. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 2002, pp. 542–545.

    Google Scholar 

  18. Weipeng P.Y. and Larson, P. Eager Aggregation and Lazy Aggregation. In Proc. 21th Int. Conf. on Very Large Data Bases, 1995, pp. 345–357.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media, LLC

About this entry

Cite this entry

Lehner, W. (2009). Query Processing in Data Warehouses. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_298

Download citation

Publish with us

Policies and ethics