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Error-bounded Sampling for Analytics on Big Sparse Data

Summary: Error-bounded stratified sampling for analytics on big sparse data with end-user accuracy guarantees. Leverages data distributions to drastically cut sample size (up to 99% smaller vs uniform) in a shared-nothing engine (SCOPE), enabling robust analytics on massive volumes. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10811
Venue
VLDB
Year
2014
Pagerank
5.6024389e-05
Overall Rank
5,252 | 63.47%
DOI
-

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