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Composable, Scalable, and Accurate Weight Summarization of Unaggregated Data Sets

Summary: Composable, scalable weight summarization for unaggregated data via a sampling-aggregation framework. Unbiased estimates for subpopulations under arbitrary predicates; no variance-optimal scheme exists, but variance improves with more aggregation; experiments beat prior methods on streams and distributed data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
9952
Venue
VLDB
Year
2009
Pagerank
4.1905499e-05
Overall Rank
12,352 | 14.16%
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
18 On Random Sampling over Joins 1999 SIGMOD 0.00092569117
184 New Sampling-Based Summary Statistics for Improving Approximate Query Answers 1998 SIGMOD 0.00036655704
3,930 Tighter Estimation using Bottom-k Sketches 2008 VLDB 6.6195837e-05
5,119 Sampling Algorithms in a Stream Operator 2005 SIGMOD 5.6774637e-05
7,545 Sketching Unaggregated Data Streams for Subpopulation-Size Queries 2007 PODS 4.7099128e-05
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