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View maintenance in a warehousing environment

Zhuge, Yue, García-Molina, Héctor, Hammer, Joachim, Widom, Jennifer·1995·Proceedings of the 1995 ACM SIGMOD international conference on Management of data - SIGMOD '95

Abstract

A warehouse is a repository of integrated information drawn from remote data sources. Since a warehouse effectively implements materialized views, we must maintain the views as the data sources are updated. This view maintenance problem differs from the traditional one in that the view definition and the base data are now decoupled. We show that this decoupling can result in anomalies if traditional algorithms are applied. We introduce a new algorithm, ECA (for Eager Compensating Algorithm), that eliminates the anomalies. ECA is based on previous incremental view maintenance algorithms, but extra compensating queries are used to eliminate anomalies. We also introduce two streamlined versions of ECA for special cases of views and updates, and we present an initial performance study that compares ECA to a view recomputation algorithm in terms of messages transmitted, data transferred, and I/O costs.

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Metadata

Authors
Zhuge, Y., García-Molina, H., Hammer, J., & Widom, J.
Venue
Proceedings of the 1995 ACM SIGMOD international conference on Management of data - SIGMOD '95
Publisher
ACM Press
Published
1995
Pages
316-327
ISSN
0730-8078 (ISSN-L)
OpenAlex
W2131165411
Cited by
430
References
0
FWCI
25.88
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: View maintenance in a warehousing environment. https://app.cochat.ai/papers/10.1145/223784.223848
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Zhuge, Y., García-Molina, H., Hammer, J., & Widom, J. (1995). View maintenance in a warehousing environment. Proceedings of the 1995 ACM SIGMOD international conference on Management of data - SIGMOD '95. https://doi.org/10.1145/223784.223848

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.