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Mining Search Engine Query Logs via Suggestion Sampling

Summary: Monte Carlo samplers for public autocomplete interfaces to reveal hidden suggestion databases. Uniform and popularity-proportional sampling enable unbiased estimation of keyword popularity, query volume, and exposure to negative content, with empirical validation on public logs and two services. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
9657
Venue
VLDB
Year
2008
Pagerank
8.0773142e-05
Overall Rank
2,813 | 80.44%
DOI
-

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

Rank Cited Paper Year Venue Pagerank
357 Random Sampling from B+ trees 1989 VLDB 0.00026020098
2,385 Comparing and Aggregating Rankings with Ties 2004 PODS 8.9247846e-05
5,140 A Random Walk Approach to Sampling Hidden Databases 2007 SIGMOD 5.668209e-05
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