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Mining a Search Engine’s Corpus: Efficient Yet Unbiased Sampling and Aggregate Estimation

Summary: Unbiased sampling and online aggregate estimation over a search-engine corpus accessible only via keyword queries. Proposes provably unbiased, low-variance methods with an order-of-magnitude lower query cost, validated by theory and experiments. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4494
Venue
SIGMOD
Year
2011
Pagerank
5.5182155e-05
Overall Rank
7,903 | 45.78%
DOI
10.1145/1989323.1989406

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{zhang_sigmod11,
        title = {{Mining a Search Engine’s Corpus: Efficient Yet Unbiased Sampling and Aggregate Estimation}},
        author = {Zhang, Mingyang and Zhang, Nan and Das, Gautam},
        series = {{SIGMOD} '11},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1989323.1989406},
        url = {https://dl.acm.org/doi/10.1145/1989323.1989406},
        year = {2011}
}

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
8,705 Progressive Deep Web Crawling Through Keyword Queries For Data Enrichment 2019 SIGMOD 5.3807913e-05
11,927 Deeper: A Data Enrichment System Powered by Deep Web 2018 SIGMOD 5.093636e-05
12,175 Aggregate Estimation Over a Microblog Platform 2014 SIGMOD 5.093636e-05
12,309 Aggregate Suppression for Enterprise Search Engines 2012 SIGMOD 5.093636e-05
13,582 Aggregate Estimations over Location Based Services 2015 VLDB -
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Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

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

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