The Fittest Survives: An Adaptive Approach to Query Optimization
Summary: An adaptive query optimizer tunes its search space and strategy to changing conditions, rather than relying on fixed procedures. It incrementally replaces cached plans for canned queries with fitter alternatives, demonstrating gains on large multijoin workloads. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Hongjun Lu (National University of Singapore)
- 2. Kian-Lee Tan (National University of Singapore)
- 3. Son Dao (Hughes Research Laboratories)
BibTeX Citation
@article{lu_vldb95,
title = {{The Fittest Survives: An Adaptive Approach to Query Optimization}},
author = {Lu, Hongjun and Tan, Kian-Lee and Dao, Son},
journal = {PVLDB},
series = {{VLDB} '95},
year = {1995}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,790 | A Characterization of the Sensitivity of Query Optimization to Storage Access Cost Parameters | 2003 | SIGMOD | 6.5068083e-05 |
| 8,163 | Learned Offline Query Planning via Bayesian Optimization | 2025 | SIGMOD | 5.4751517e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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