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Sampling Big Ideas in Query Optimization

Summary: Survey of weighted and coordinated sampling methods for query optimization, using samples as concise summaries to scale complex analytics. Emphasizes simple, practical algorithms for streaming and distributed data that enable low‑overhead, coordinated estimates across queries and partitions. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1933
Venue
PODS
Year
2023
Pagerank
5.4151005e-05
Overall Rank
8,413 | 42.48%
DOI
10.1145/3584372.3589935

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{cohen_pods23,
        address = {New York, NY, USA},
        series = {{PODS} '23},
        title = {{Sampling Big Ideas in Query Optimization}},
        url = {https://dl.acm.org/doi/10.1145/3584372.3589935},
        doi = {10.1145/3584372.3589935},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Cohen, Edith},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
8,287 Sampling Methods for Inner Product Sketching 2024 VLDB 5.4346419e-05
8,824 Practical Dynamic Extension for Sampling Indexes 2023 SIGMOD 5.3497363e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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