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Global Optimization of Histograms

Summary: Global optimization of histograms across multiple attributes, allocating buckets adaptively to skew and usage to minimize error. DP and greedy algorithms versus equal-bucket baselines; results show large reductions, up to 100x for skewed data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3271
Venue
SIGMOD
Year
2001
Pagerank
0.00013856211
Overall Rank
1,120 | 92.22%
DOI
-

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