So Who Won? Dynamic Max Discovery with the Crowd
Summary: Dynamic max discovery in a crowdsourcing DB relies on human pairwise judgments under latency and cost. Optimal max selection and extra-vote gathering are NP-hard; the paper offers heuristics for max-finding and vote acquisition with experiments. (summarized by gpt-5-nano on Feb 09 2026)
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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 |
|---|---|---|---|---|
| 94 | CrowdDB: Answering Queries with Crowdsourcing | 2011 | SIGMOD | 0.00051013264 |
| 267 | Human-powered Sorts and Joins | 2012 | VLDB | 0.00029690405 |
| 697 | Human-Assisted Graph Search: It’s Okay to Ask Questions | 2011 | VLDB | 0.00018043655 |
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