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...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Recommended Reading
Celko J. Joe Celko’s Data Warehouse and Analytic Queries in SQL. Morgan Kaufmann, 2006.
Chan C.-Y. 1998.Bitmap index design and evaluation. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pp. 355–366.
Chaudhuri S.and Dayal U. 1997.An Overview of Data Warehousing and OLAP Technology. ACM SIGMOD Rec., 26(1):65–74,
Clement T.Y. and Meng W. Principles of Database Query Processing for Advanced Applications. Morgan Kaufmann, 1997.
Data Mining Extensions (DMX) Reference. Available at: http://msdn2.microsoft.com/en-us/library/ms132058.aspx
Graefe G. 1993, Query Evaluation Techniques for Large Databases. In ACM Comput. Surv., 25(2), S. 73–170.
Gray J. et al. The Lowell Database Research Self Assessment, June 2003. Available at: http://research.microsoft.com/∼gray/lowell/
Gupta A. and Mumick I. Materialized Views: Techniques, Implementations and Applications. MIT Press, Cambridge, MA, 1999.
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.
Inmon W.H. Building the Data Warehouse. 2nd edn, Wiley, NY, USA.
Niemiec R. Oracle Database 10g Performance Tuning Tips & Techniques, 2007.
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.
N.N. Multidimensional Expressions (MDX) Reference. Available at: http://msdn2.microsoft.com/en-us/library/ms145506.aspx.
Roussopoulos N. 1982.The logical access path schema of a database. In IEEE Trans. Softw. Eng., 8, (6)S.563–573,
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.
Valduriez P. 1987.Join indices. ACM Trans. Database Syst., 12(2):218–246,
Weininger A. Efficient execution of joins in a star schema. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 2002, pp. 542–545.
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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights 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
DOI: https://doi.org/10.1007/978-0-387-39940-9_298
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-35544-3
Online ISBN: 978-0-387-39940-9
eBook Packages: Computer ScienceReference Module Computer Science and Engineering