Genetic algorithm for materialized view selection in data warehouse environments

Chuan Zhang, Jian Yang

Research output: Chapter in Book/Report/Conference proceedingChapter

34 Citations (Scopus)

Abstract

Data Warehouse applications use a large number of materialized views to assist a Data Warehouse to perform well. But how to select views to be materialized is challenging. Several heuristic algorithms have been proposed in the past to tackle with this problem. In this paper, we propose a completely different approach, Genetic Algorithm, to choose materialized views and demonstrate that it is practical and effective compared with heuristic approaches.

Original languageEnglish
Title of host publicationData Warehousing and Knowledge Discovery - 1st International Conference, DaWaK 1999, Proceedings
EditorsMukesh Mohania, Min A. Tjoa
Place of PublicationBerlin
PublisherSpringer, Springer Nature
Pages116-125
Number of pages10
Volume1676
ISBN (Electronic)9783540482987
ISBN (Print)3540664580, 9783540664581
DOIs
Publication statusPublished - 1999
Externally publishedYes
Event1st International Conference on Data Warehousing and Knowledge Discovery, DaWaK 1999 - Florence, Italy
Duration: 30 Aug 19991 Sep 1999

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1676
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other1st International Conference on Data Warehousing and Knowledge Discovery, DaWaK 1999
CountryItaly
CityFlorence
Period30/08/991/09/99

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  • Cite this

    Zhang, C., & Yang, J. (1999). Genetic algorithm for materialized view selection in data warehouse environments. In M. Mohania, & M. A. Tjoa (Eds.), Data Warehousing and Knowledge Discovery - 1st International Conference, DaWaK 1999, Proceedings (Vol. 1676, pp. 116-125). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1676). Berlin: Springer, Springer Nature. https://doi.org/10.1007/3-540-48298-9_12