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Cited by 668

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

The Gamma database machine project

DeWitt, D.J., Ghandeharizadeh, S., Schneider, D.A., Bricker, A., Hsiao, H.-I., Rasmussen, R.·1990·IEEE Transactions on Knowledge and Data Engineering

Abstract

The design of the Gamma database machine and the techniques employed in its implementation are described. Gamma is a relational database machine currently operating on an Intel iPSC/2 hypercube with 32 processors and 32 disk drives. Gamma employs three key technical ideas which enable the architecture to be scaled to hundreds of processors. First, all relations are horizontally partitioned across multiple disk drives, enabling relations to be scanned in parallel. Second, parallel algorithms based on hashing are used to implement the complex relational operators, such as join and aggregate functions. Third, dataflow scheduling techniques are used to coordinate multioperator queries. By using these techniques, it is possible to control the execution of very complex queries with minimal coordination. The design of the Gamma software is described and a thorough performance evaluation of the iPSC/s hypercube version of Gamma is presented.>

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Metadata

Authors
DeWitt, D. J., Ghandeharizadeh, S., Schneider, D. A., Bricker, A., Hsiao, H.-I., & Rasmussen, R.
Venue
IEEE Transactions on Knowledge and Data Engineering
Published
1990-03
Volume
2
Issue
1
Pages
44-62
ISSN
1041-4347 (ISSN-L)
OpenAlex
W2080427212
Cited by
668
References
9
FWCI
52.02
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The Gamma database machine project. https://app.cochat.ai/papers/10.1109/69.50905
The paper
DeWitt, D. J., Ghandeharizadeh, S., Schneider, D. A., Bricker, A., Hsiao, H.-I., & Rasmussen, R. (1990). The Gamma database machine project. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/69.50905

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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.