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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
7423
Venue
SIGMOD
Year
2026
Pagerank
4.1945683e-05
Overall Rank
10,113 | 29.65%
DOI
10.1145/3769827

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