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Error-bounded Sampling for Analytics on Big Sparse Data

Summary: Introduces error-bounded stratified sampling for aggregation over massive, wide-range sparse data, preserving user-specified error guarantees. Distribution-aware strata reduce sample size by up to 99% versus uniform sampling, with robust performance in Microsoft’s search-query platform. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
10999
Venue
VLDB
Year
2014
Pagerank
6.557612e-05
Overall Rank
4,696 | 67.79%
DOI
10.14778/2733004.2733012

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{yan_vldb14,
        title = {{Error-bounded Sampling for Analytics on Big Sparse Data}},
        author = {Yan, Ying and Chen, Liang Jeff and Zhang, Zheng},
        journal = {PVLDB},
        series = {{VLDB} '14},
        volume = {7},
        number = {13},
        pages = {1508--1519},
        doi = {10.14778/2733004.2733012},
        url = {https://doi.org/10.14778/2733004.2733012},
        year = {2014}
}

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