Exploiting Statistics on Query Expressions for Optimization
Summary: Extends the optimizer to exploit statistics on intermediate query expressions (not just base-table stats), yielding better plan quality. Proposes a workload-driven feature selection to identify a small, impactful subset of expressions' statistics, with validation on SQL Server 2000. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Nicolas Bruno (Columbia University)
- 2. Surajit Chaudhuri (Microsoft)
BibTeX Citation
@inproceedings{bruno_sigmod02,
title = {{Exploiting Statistics on Query Expressions for Optimization}},
author = {Bruno, Nicolas and Chaudhuri, Surajit},
series = {{SIGMOD} '02},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/564691.564722},
url = {https://dl.acm.org/doi/10.1145/564691.564722},
year = {2002}
}
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