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Coordinated Weighted Sampling for Estimating Aggregates Over Multiple Weight Assignments

Summary: Coordinated weighted sampling for vector-weighted keys enables accurate aggregates across multiple weight assignments (time snapshots, multi-location requests). A unified framework with estimators orders of magnitude tighter than single-weight designs; empirically validated on IP networks and stock quotes data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h380ab1476812806c
Venue
VLDB
Year
2009
Pagerank
5.8813101e-05
Overall Rank
6,118 | 58.87%
DOI
10.14778/1687627.1687701

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{cohen_vldb09,
        title = {{Coordinated Weighted Sampling for Estimating Aggregates Over Multiple Weight Assignments}},
        author = {Cohen, Edith and Kaplan, Haim and Sen, Subhabrata},
        journal = {PVLDB},
        series = {{VLDB} '09},
        doi = {10.14778/1687627.1687701},
        url = {https://doi.org/10.14778/1687627.1687701},
        year = {2009}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
3,858 Is Min-Wise Hashing Optimal for Summarizing Set Intersection? 2014 PODS 6.9676872e-05
8,421 Sampling Methods for Inner Product Sketching 2024 VLDB 5.3350162e-05
8,553 Sampling Big Ideas in Query Optimization 2023 PODS 5.3158971e-05
12,653 Get the Most out of Your Sample: Optimal Unbiased Estimators using Partial Information 2011 PODS 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 8 of 8 cited papers.

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

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