Towards a Robust Query Optimizer: A Principled and Practical Approach
Summary: Proposes robust query optimization by treating cardinality estimation probabilistically rather than exact. A Bayesian, sample-based estimator from precomputed samples captures correlations and enables performance/predictability tradeoffs, integrating with optimizers. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Brian Babcock (Stanford University)
- 2. Surajit Chaudhuri (Microsoft)
BibTeX Citation
@inproceedings{babcock_sigmod05,
title = {{Towards a Robust Query Optimizer: A Principled and Practical Approach}},
author = {Babcock, Brian and Chaudhuri, Surajit},
series = {{SIGMOD} '05},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1066157.1066172},
url = {https://dl.acm.org/doi/10.1145/1066157.1066172},
year = {2005}
}
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