Synopses for Query Optimization: A Space-Complexity Perspective
Summary: Information-theoretic analysis of synopsis space: histograms suffice for single-table selections but are fundamentally limited for joins. For key–foreign-key joins, small precomputed samples yield nearly space-optimal probabilistic guarantees; experiments confirm samples outperform histograms as joins increase. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Raghav Kaushik (Microsoft)
- 2. Raghu Ramakrishnan (University of Wisconsin)
- 3. Venkatesan T. Chakaravarthy (University of Wisconsin)
BibTeX Citation
@inproceedings{kaushik_pods04,
address = {New York, NY, USA},
series = {{PODS} '04},
title = {{Synopses for Query Optimization: A Space-Complexity Perspective}},
url = {https://dl.acm.org/doi/10.1145/1055558.1055586},
doi = {10.1145/1055558.1055586},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Kaushik, Raghav and Ramakrishnan, Raghu and Chakaravarthy, Venkatesan T.},
year = {2004}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 401 | Deep Unsupervised Cardinality Estimation | 2020 | VLDB | 0.00019092557 |
| 4,349 | ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads | 2024 | VLDB | 6.7504619e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 19 of 19 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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