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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
4432
Venue
SIGMOD
Year
2011
Pagerank
4.6249533e-05
Overall Rank
7,890 | 45.12%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
8,678 Progressive Deep Web Crawling Through Keyword Queries For Data Enrichment 2019 SIGMOD 4.4702119e-05
11,722 Deeper: A Data Enrichment System Powered by Deep Web 2018 SIGMOD 4.1945683e-05
11,977 Aggregate Estimation Over a Microblog Platform 2014 SIGMOD 4.1945683e-05
12,112 Aggregate Suppression for Enterprise Search Engines 2012 SIGMOD 4.1945683e-05
13,381 Aggregate Estimations over Location Based Services 2015 VLDB -
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Outgoing Citations (Sorted by Pagerank)

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

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