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Approximating and Testing k-Histogram Distributions in Sub-linear Time

Summary: Sublinear-time, sample-efficient algorithms that from samples produce a k-interval piecewise-constant approximation minimizing L2 error to an unknown distribution over [n]. Also gives testers distinguishing k-histograms from distributions ε-far in L1 or L2 with improved complexity. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1561
Venue
PODS
Year
2012
Pagerank
4.9816401e-05
Overall Rank
6,637 | 53.83%
DOI
-

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
269 Fast Incremental Maintenance of Approximate Histograms 1997 VLDB 0.00029656549
325 The History of Histograms (abridged) 2003 VLDB 0.00027378328
326 Optimal Histograms with Quality Guarantees 1998 VLDB 0.00027358981
530 Random Sampling for Histogram Construction: How much is enough? 1998 SIGMOD 0.00020803682
852 Dynamic Multidimensional Histograms 2002 SIGMOD 0.00015941524
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