Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Indexing of Data Warehouses

  • Theodore Johnson
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_200

Synonyms

Data warehouse indexing

Definition

Indices are data structures especially designed to allow rapid access to data in large databases. Data warehouses are typically used to perform intensive analyses of very large data sets. Several indices, such as projection indices, bitmap indices, bitslice indices, and summary indices, have been developed to address the special needs of data warehousing and are presented in this entry.

Historical Background

Data warehouses were developed to capture operational information, store it over a long period, and provide support for intensive analysis of historical data. The special needs of data warehouses – very large data sets, high-dimensional data, and the extensive use of categorical data – have led to the development of specialized indices intended for use in data warehouses. The use of these indices was pioneered in such systems as Model 204 and Sybase IQ.

Foundations

Indices, such as B-trees, R-trees, quad-trees, hash tables, and inverted...

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Recommended Reading

  1. 1.
    Amer-Yahia S, Johnson T. Optimizing queries on compressed bitmaps. In: Proceedings of the 26th International Conference on Very Large Data Bases; 2000. p. 329–38.Google Scholar
  2. 2.
    Apaydin T, Canahuate G, Ferhatosmanoglu H, Tosun A. Approximate encoding for direct access and query processing over compressed bitmaps. In: Proceedings of the 32nd International Conference on Very Large Data Bases; 2006. p. 846–57.Google Scholar
  3. 3.
    Johnson T. Coarse indices for a tape-based data warehouse. In: Proceedings of the 14th International Conference on Data Engineering; 1998. p. 231–40.Google Scholar
  4. 4.
    Johnson T, Shasha D. Some approaches to index design for cube forest. IEEE Data Eng Bull. 1997;20(1):27–35.Google Scholar
  5. 5.
    Kline N, Snodgrass R. Computing temporal aggregates. In: Proceedings of the 11th International Conference on Data Engineering; 1995. p. 222–31.Google Scholar
  6. 6.
    Kotidis Y, Roussopoulos N. An alternative storage organization for ROLAP aggregate views based on cubetrees. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1998. p. 249–58.CrossRefGoogle Scholar
  7. 7.
    Moerkotte G. Small materialized aggregates: a light weight index structure for data warehousing. In: Proceedings of the 24th International Conference on Very Large Data Bases; 1998. p. 476–87.Google Scholar
  8. 8.
    O’Neil P, Quass D. Improved query performance with variant indices. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1997. p. 38–49.Google Scholar
  9. 9.
    Rdb7: Performance enhancements for 32 and 64 bit systems. Available online at. http://www.oracle.com/products/servers/rdb/html/fsvlm.html
  10. 10.
    Wu K, Koegler WS, Chen J, Shoshani A. Using bitmap index for interactive exploration of large datasets. In: Proceedings of the 15th International Conference on Scientific and Statistical Database Management; 2003. p. 65–74.Google Scholar
  11. 11.
    Wu K, Otoo EJ, Shoshani A. Compressing bitmap indexes for faster search operations. In: Proceedings of the 14th International Conference on Scientific and Statistical Database Management; 2002. p. 99–108.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.AT&T Labs – ResearchFlorham ParkUSA

Section editors and affiliations

  • Torben Bach Pedersen
    • 1
  • Stefano Rizzi
    • 2
  1. 1.Department of Computer ScienceAalborg UniversityAalborgDenmark
  2. 2.DISIUniversity of BolognaBolognaItaly