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Approximate Sketches

Summary: Per-table transformers learn the joint attribute distribution to yield a multidimensional sketch for selections. This enables join cardinality with arbitrary filters, scales linearly with table count, and matches exact-sketch accuracy with lower overhead. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6937
Venue
SIGMOD
Year
2024
Pagerank
5.2308295e-05
Overall Rank
9,724 | 33.29%
DOI
10.1145/3639321

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{tsan_sigmod24,
        title = {{Approximate Sketches}},
        author = {Tsan, Brian and Datta, Asoke and Izenov, Yesdaulet and Rusu, Florin},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3639321},
        url = {https://dl.acm.org/doi/10.1145/3639321},
        year = {2024}
}

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