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Space- and Time-Efficient Deterministic Algorithms for Biased Quantiles over Data Streams

Summary: First deterministic one-pass algorithms for biased quantiles on data streams, providing skew-aware accuracy (more precise at extremes) with near-optimal sublinear space. Amortized updates are near-constant, practical and empirically use less space and are fast to maintain. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1398
Venue
PODS
Year
2006
Pagerank
7.8239173e-05
Overall Rank
2,955 | 79.45%
DOI
-

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