Aqua: A Fast Decision Support System Using Approximate Query Answers
Summary: Aqua provides fast approximate aggregation for OLAP by rewriting queries to run on precomputed synopses stored in the DBMS. Synopses are incrementally maintained and come with quality guarantees, enabling DBMS-agnostic deployment atop commercial RDBMS. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Swarup Acharya (AT&T)
- 2. Phillip B. Gibbons (AT&T)
- 3. Viswanath Poosala (AT&T)
BibTeX Citation
@article{acharya_vldb99,
title = {{Aqua: A Fast Decision Support System Using Approximate Query Answers}},
author = {Acharya, Swarup and Gibbons, Phillip B. and Poosala, Viswanath},
journal = {PVLDB},
series = {{VLDB} '99},
pages = {754},
year = {1999}
}
Incoming Citations (Sorted by Pagerank)
Showing 22 of 22 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9 | Online Aggregation | 1997 | SIGMOD | 0.00077458002 |
| 136 | Join Synopses for Approximate Query Answering | 1999 | SIGMOD | 0.00030123303 |
| 149 | New Sampling-Based Summary Statistics for Improving Approximate Query Answers | 1998 | SIGMOD | 0.00029226907 |
| 235 | Fast Incremental Maintenance of Approximate Histograms | 1997 | VLDB | 0.00023783792 |
| 553 | Congressional Samples for Approximate Answering of Group-By Queries | 2000 | SIGMOD | 0.00016590619 |
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