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SplineSketch: Even More Accurate Quantiles with Error Guarantees

Summary: Introduces SplineSketch, a streaming quantile sketch for numeric data that maintains a dynamic subdivision of the value range and fits a monotone cubic spline to the empirical distribution for much tighter interpolation. Achieves near‑optimal uniformly bounded rank-error guarantees while empirically beating t‑digest by 2–20×. (summarized by gpt-5-mini on Feb 11 2026)

Paper ID
7613
Venue
SIGMOD
Year
2026
Pagerank
5.2434488e-05
Overall Rank
9,618 | 34.02%
DOI
10.1145/3769827

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{ukasiewicz_sigmod26,
        title = {{SplineSketch: Even More Accurate Quantiles with Error Guarantees}},
        author = {Łukasiewicz, Aleksander and Tětek, Jakub and Veselý, Pavel},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3769827},
        url = {https://dl.acm.org/doi/10.1145/3769827},
        year = {2026}
}

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Rank Citing Paper Year Venue Pagerank
10,295 Sublime: Sublinear Error & Space for Unbounded Skewed Streams 2026 SIGMOD 5.093636e-05
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