CrowdMiner: Mining association rules from the crowd
Summary: CrowdMiner introduces a novel crowd-mining algorithm for association rules from crowd data, not static databases. An iterative, question-driven querying process maximizes knowledge gain, demonstrated via a Well-Being portal mining health trends among conference participants. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yael Amsterdamer
- 2. Yael Grossman
- 3. Tova Milo
- 4. Pierre Senellart
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,116 | Crowdsourced Data Management: Overview and Challenges | 2017 | SIGMOD | 4.8219732e-05 |
| 7,285 | Subjective Knowledge Base Construction Powered By Crowdsourcing and Knowledge Base | 2018 | SIGMOD | 4.7702194e-05 |
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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.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 36 | Fast Algorithms for Mining Association Rules | 1994 | VLDB | 0.00076114894 |
| 94 | CrowdDB: Answering Queries with Crowdsourcing | 2011 | SIGMOD | 0.00051273089 |
| 118 | Answering Queries using Humans, Algorithms and Databases | 2011 | CIDR | 0.00045860394 |
| 473 | Sampling Large Databases for Association Rules | 1996 | VLDB | 0.00022304724 |
| 3,104 | Crowd Mining | 2013 | SIGMOD | 7.5577057e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,726 | Crowdsourcing Analytics with CrowdCur | 2018 | SIGMOD | 4.1905499e-05 |
| 3,939 | CrowdQ: Crowdsourced Query Understanding | 2013 | CIDR | 6.6099823e-05 |
| 4,414 | CrowdMatcher: Crowd-Assisted Schema Matching | 2014 | SIGMOD | 6.1975384e-05 |
| 13,553 | MobileMiner: A Real World Case Study of Data Mining in Mobile Communication | 2009 | SIGMOD | - |
| 6,622 | Exploratory Mining via Constrained Frequent Set Queries | 1999 | SIGMOD | 4.9846111e-05 |
| 9,862 | Large Scale Graph Mining with G-Miner | 2019 | SIGMOD | 4.2641627e-05 |
| 8,690 | Managing General and Individual Knowledge in Crowd Mining Applications | 2015 | CIDR | 4.4618888e-05 |
| 9,214 | Ontology Assisted Crowd Mining | 2014 | VLDB | 4.3680645e-05 |
| 7,224 | OASSIS: Query Driven Crowd Mining | 2014 | SIGMOD | 4.791302e-05 |
| 3,104 | Crowd Mining | 2013 | SIGMOD | 7.5577057e-05 |