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)
Incoming Non-self Citations Over Time
Authors
- 1. Aleksander Łukasiewicz (Charles University)
- 2. Jakub Tětek (INSAIT; Sofia University St. Kliment Ohridski)
- 3. Pavel Veselý (Charles University)
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}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| 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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Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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