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Sapprox: Enabling Efficient and Accurate Approximations on Sub-datasets with Distribution-aware Online Sampling

Summary: Sapprox enables efficient, accurate approximations on arbitrary sub-datasets via distribution-aware sampling. Uses a probabilistic map to flatten subsets, applies unequal-probability sampling, and optimizes unit size, yielding 20x speedups in Hadoop. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11583
Venue
VLDB
Year
2017
Pagerank
5.2518295e-05
Overall Rank
9,591 | 34.20%
DOI
10.14778/3021924.3021925

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{zhang_vldb17,
        title = {{Sapprox: Enabling Efficient and Accurate Approximations on Sub-datasets with Distribution-aware Online Sampling}},
        author = {Zhang, Xuhong and Wang, Jun and Yin, Jiangling},
        journal = {PVLDB},
        series = {{VLDB} '17},
        volume = {10},
        number = {3},
        pages = {109--120},
        doi = {10.14778/3021924.3021925},
        url = {https://doi.org/10.14778/3021924.3021925},
        year = {2017}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
7,129 Marviq: Quality-Aware Geospatial Visualization of Range-Selection Queries Using Materialization 2020 SIGMOD 5.694968e-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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