Adaptive Parallel Aggregation Algorithms
Summary: Adaptive parallel aggregation algorithms adjust dynamically to observed grouping selectivities. Analytical modeling and a cluster prototype show strong performance across selectivities and skew, outperforming traditional approaches on some datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ambuj Shatdal (University of Wisconsin)
- 2. Jeffrey F. Naughton (University of Wisconsin)
BibTeX Citation
@inproceedings{shatdal_sigmod95,
title = {{Adaptive Parallel Aggregation Algorithms}},
author = {Shatdal, Ambuj and Naughton, Jeffrey F.},
series = {{SIGMOD} '95},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/223784.223801},
url = {https://dl.acm.org/doi/10.1145/223784.223801},
year = {1995}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 811 | A Taxonomy and Performance Model of Data Skew Effects in Parallel Joins | 1991 | VLDB | 0.00013859761 |
| 2,163 | Parallel Algorithms and Their Implementation in MICRONET | 1982 | VLDB | 9.0545802e-05 |
| 2,184 | Managing Memory to Meet Multiclass Workload Response Time Goals | 1993 | VLDB | 9.0045033e-05 |
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