Proactive Re-Optimization
Summary: Proactive re-optimization computes bounding boxes around statistics to bound estimate errors, selects plans robust to deviations for skewed/correlated data, and enables rapid online statistics collection during execution. Rio prototype shows up to 3× gains over traditional re-optimizers on complex queries. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Shivnath Babu (Stanford University)
- 2. Pedro Bizarro (University of Wisconsin)
- 3. David DeWitt (University of Wisconsin)
BibTeX Citation
@inproceedings{babu_sigmod05,
title = {{Proactive Re-Optimization}},
author = {Babu, Shivnath and Bizarro, Pedro and DeWitt, David},
series = {{SIGMOD} '05},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1066157.1066171},
url = {https://dl.acm.org/doi/10.1145/1066157.1066171},
year = {2005}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 51 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 12,681 | Asking the Right Questions: Model-driven Optimization using Probes | 2006 | PODS | 5.093636e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 18 of 18 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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