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Mining Search Engine Query Logs via Suggestion Sampling

Summary: Black-box Monte Carlo algorithms sample hidden auto-completion databases through public suggestion APIs, either uniformly or popularity-proportionally. Enables unbiased mining of query logs—keyword popularity, commercial-query prevalence, and negative-content exposure—without direct database access. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
9848
Venue
VLDB
Year
2008
Pagerank
8.2608808e-05
Overall Rank
2,683 | 81.60%
DOI
10.14778/1453856.1453868

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{baryossef_vldb08,
        title = {{Mining Search Engine Query Logs via Suggestion Sampling}},
        author = {Bar-Yossef, Ziv and Gurevich, Maxim},
        journal = {PVLDB},
        series = {{VLDB} '08},
        pages = {54},
        doi = {10.14778/1453856.1453868},
        url = {https://doi.org/10.14778/1453856.1453868},
        year = {2008}
}

Incoming Citations (Sorted by Pagerank)

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Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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

Rank Cited Paper Year Venue Pagerank
467 Random Sampling from B+ trees 1989 VLDB 0.00018002218
2,652 Comparing and Aggregating Rankings with Ties 2004 PODS 8.291138e-05
5,868 A Random Walk Approach to Sampling Hidden Databases 2007 SIGMOD 6.0613238e-05
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