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Estimating the Impact of Unknown Unknowns on Aggregate Query Results

Summary: Leverages source overlap to estimate unobserved data's count and values, quantifying unknown-unknown impact on simple aggregates. Parameter-free, distribution-agnostic; enables uncertainty-aware assessment of integrated-data results without priors. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5162
Venue
SIGMOD
Year
2016
Pagerank
5.7635226e-05
Overall Rank
6,819 | 53.22%
DOI
10.1145/2882903.2882909

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{chung_sigmod16,
        title = {{Estimating the Impact of Unknown Unknowns on Aggregate Query Results}},
        author = {Chung, Yeounoh and Mortensen, Michael Lind and Binnig, Carsten and Kraska, Tim},
        series = {{SIGMOD} '16},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2882903.2882909},
        url = {https://dl.acm.org/doi/10.1145/2882903.2882909},
        year = {2016}
}

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