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)
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Authors
- 1. Edith Cohen
- 2. Nick Duffield
- 3. Haim Kaplan
- 4. Carsten Lund
- 5. Mikkel Thorup
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Showing 5 of 5 cited papers.
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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