View derivation graph with edge fitting for adaptive data warehousing

Ioana Stanoi, Divyakant Agrawal, Amr El Abbadi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper we propose adaptive data warehouse mainte- nance, based on the optimistic partial replication of base source data that has already been used in deriving view tuples. Our method reduces local computation at the view as well as communication with the outside sources, and lowers the execution load on the base sources, which leads to a more up-to-date state of the data warehouse view.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages67-76
Number of pages10
Volume1874
ISBN (Print)3540679804, 9783540679806
Publication statusPublished - 2000
Externally publishedYes
Event2nd International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2000 - London, United Kingdom
Duration: 4 Sep 20006 Sep 2000

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1874
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other2nd International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2000
CountryUnited Kingdom
CityLondon
Period4/9/006/9/00

Fingerprint

Data Warehousing
Data warehouses
Data Warehouse
Graph in graph theory
Local Computation
Replication
Communication
Partial

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Stanoi, I., Agrawal, D., & El Abbadi, A. (2000). View derivation graph with edge fitting for adaptive data warehousing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1874, pp. 67-76). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1874). Springer Verlag.

View derivation graph with edge fitting for adaptive data warehousing. / Stanoi, Ioana; Agrawal, Divyakant; El Abbadi, Amr.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1874 Springer Verlag, 2000. p. 67-76 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1874).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Stanoi, I, Agrawal, D & El Abbadi, A 2000, View derivation graph with edge fitting for adaptive data warehousing. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 1874, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1874, Springer Verlag, pp. 67-76, 2nd International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2000, London, United Kingdom, 4/9/00.
Stanoi I, Agrawal D, El Abbadi A. View derivation graph with edge fitting for adaptive data warehousing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1874. Springer Verlag. 2000. p. 67-76. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Stanoi, Ioana ; Agrawal, Divyakant ; El Abbadi, Amr. / View derivation graph with edge fitting for adaptive data warehousing. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1874 Springer Verlag, 2000. pp. 67-76 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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