Encyclopedia of Database Systems

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

Cube Implementations

  • Konstantinos Morfonios
  • Yannis Ioannidis
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_91

Synonyms

Cube materialization; Cube precomputation

Definition

Cube implementation involves the procedures of computation, storage, and manipulation of a data cube, which is a disk structure that stores the results of the aggregate queries that group the tuples of a fact table on all possible combinations of its dimension attributes. For example in Fig.  1a, assuming that R is a fact table that consists of three dimensions (A, B, C) and one measure M (see definitional entry for Measure), the corresponding cube of R appears in Fig.  1b. Each cube node (i.e., view that belongs to the data cube) stores the results of a particular aggregate query as shown in Fig.  1b. Clearly, if D denotes the number of dimensions of a fact table, the number of all possible aggregate queries is 2 D; hence, in the worst case, the size of the data cube is exponentially larger with respect to D than the size of the original fact table. In typical applications, this may be in the order of gigabytes or even...
This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Agarwal S, Agrawal R, Deshpande P, Gupta A, Naughton JF, Ramakrishnan R, Sarawagi S. On the computation of multidimensional aggregates. In: Proceedings of the 22th International Conference on Very Large Data Bases; 1996. p. 506–21.Google Scholar
  2. 2.
    Beyer KS, Ramakrishnan R. Bottom-up computation of sparse and iceberg CUBEs. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1999. p. 359–70.CrossRefGoogle Scholar
  3. 3.
    Gray J, Bosworth A, Layman A, Pirahesh H. Data cube: a relational aggregation operator generalizing group-by, cross-tab, and sub-total. In: Proceedings of the 12th International Conference on Data Engineering; 1996. p. 152–9.Google Scholar
  4. 4.
    Gupta H. Selection of views to materialize in a data warehouse. In: Proceedings of the 6th International Conference on Database Theory; 1997. p. 98–112.Google Scholar
  5. 5.
    Gupta H, Mumick IS. Selection of views to materialize under a maintenance cost constraint. In: Proceedings of the 7th International Conference on Database Theory; 1999. p. 453–70.Google Scholar
  6. 6.
    Harinarayan V, Rajaraman A, Ullman JD. Implementing data cubes efficiently. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1996. p. 205– 16.Google Scholar
  7. 7.
    Kotsis N, McGregor DR. Elimination of redundant views in multidimensional aggregates. In: Proceedings of the 2nd International Conference on Data Warehousing and Knowledge Discovery; 2000. p. 146–61.CrossRefGoogle Scholar
  8. 8.
    Lakshmanan LVS, Pei J, Zhao Y. QC-Trees: an efficient summary structure for semantic OLAP. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2003. p. 64–75.Google Scholar
  9. 9.
    Lee KY, Kim MH. Efficient incremental maintenance of data cubes. In: Proceedings of the 32nd International Conference on Very Large Data Bases; 2006. p. 823–33.Google Scholar
  10. 10.
    Morfonios K, Ioannidis Y. CURE for cubes: cubing using a ROLAP engine. In: Proceedings of the 32nd International Conference on Very Large Data Bases; 2006. p. 379–90.Google Scholar
  11. 11.
    Morfonios K, Ioannidis Y. Supporting the data cube lifecycle: the power of ROLAP. VLDB J. 2008;17(4):729–64.CrossRefGoogle Scholar
  12. 12.
    Morfonios K, Konakas S, Ioannidis Y, Kotsis N. ROLAP implementations of the data cube. ACM Comput Surv. 2007;39(4):12.CrossRefGoogle Scholar
  13. 13.
    Mumick IS, Quass D, Mumick BS. Maintenance of data cubes and summary tables in a warehouse. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1997. p. 100–11.Google Scholar
  14. 14.
    Poosala V, Ganti V. Fast approximate answers to aggregate queries on a data cube. In: Proceedings of the 11th International Conference on Scientific and Statistical Database Management; 1999. p. 24–33.Google Scholar
  15. 15.
    Ross KA, Srivastava D. Fast computation of sparse datacubes. In: Proceedings of the 23th International Conference on Very Large Data Bases; 1997. p. 116–25.Google Scholar
  16. 16.
    Shao Z, Han J, Xin D. MM-Cubing: computing iceberg cubes by factorizing the lattice Space. In: Proceedings of the 16th International Conference on Scientific and Statistical Database Management; 2004. p. 213–22.Google Scholar
  17. 17.
    Sismanis Y, Deligiannakis A, Roussopoulos N, Kotidis Y. Dwarf: shrinking the PetaCube. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2002. p. 464–75.Google Scholar
  18. 18.
    Sismanis Y, Roussopoulos N. The complexity of fully materialized coalesced cubes. In: Proceedings of the 30th International Conference on Very Large Data Bases; 2004. p. 540–51.Google Scholar
  19. 19.
    Vitter JS, Wang M. Approximate computation of multidimensional aggregates of sparse data using wavelets. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1999. p. 193–204.CrossRefGoogle Scholar
  20. 20.
    Wang W, Feng J, Lu H, Yu JX. Condensed cube: an efficient approach to reducing data cube size. In: Proceedings of the 18th International Conference on Data Engineering; 2002. p. 155–65.Google Scholar
  21. 21.
    Zhao Y, Deshpande P, Naughton JF. An array-based algorithm for simultaneous multidimensional aggregates. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1997. p. 159–70.Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.OracleRedwood CityUSA
  2. 2.University of AthensAthensGreece

Section editors and affiliations

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