OASSIS: Query Driven Crowd Mining
Summary: OASSIS enables query-driven crowd mining for frequent, significant patterns via general questions to crowd data. It merges ontological knowledge with user history, offers a declarative query language and efficient evaluator, and returns concise answers while minimizing crowd questions through an interactive UI, validated on real and synthetic data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yael Amsterdamer
- 2. Susan B. Davidson
- 3. Tova Milo
- 4. Slava Novgorodov
- 5. Amit Somech
Incoming Citations (Sorted by Pagerank)
Showing 9 of 9 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,788 | Lenses: An On-Demand Approach to ETL | 2015 | VLDB | 5.3256103e-05 |
| 7,116 | Crowdsourced Data Management: Overview and Challenges | 2017 | SIGMOD | 4.8219732e-05 |
| 7,780 | A Natural Language Interface for Querying General and Individual Knowledge | 2015 | VLDB | 4.6489024e-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 |
| 11,730 | ZigZag: Supporting Similarity Queries on Vector Space Models | 2018 | SIGMOD | 4.1905499e-05 |
| 11,824 | DOCS: Domain-Aware Crowdsourcing System | 2017 | VLDB | 4.1905499e-05 |
| 11,872 | December: A Declarative Tool for Crowd Member Selection | 2016 | VLDB | 4.1905499e-05 |
| 11,920 | NL2CM: A Natural Language Interface to Crowd Mining | 2015 | SIGMOD | 4.1905499e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 265 | CrowdER: Crowdsourcing Entity Resolution | 2012 | VLDB | 0.00029904018 |
| 404 | Mining Generalized Association Rules | 1995 | VLDB | 0.00024159651 |
| 854 | So Who Won? Dynamic Max Discovery with the Crowd | 2012 | SIGMOD | 0.00015879917 |
| 863 | Leveraging Transitive Relations for Crowdsourced Joins | 2013 | SIGMOD | 0.00015793243 |
| 1,154 | CrowdScreen: Algorithms for Filtering Data with Humans | 2012 | SIGMOD | 0.00013616867 |
| 1,491 | CDAS: A Crowdsourcing Data Analytics System | 2012 | VLDB | 0.00011685731 |
| 2,255 | Counting with the Crowd | 2013 | VLDB | 9.1846281e-05 |
| 3,104 | Crowd Mining | 2013 | SIGMOD | 7.5577057e-05 |
| 7,112 | Answering Planning Queries with the Crowd | 2013 | VLDB | 4.8227734e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,491 | CDAS: A Crowdsourcing Data Analytics System | 2012 | VLDB | 0.00011685731 |
| 7,577 | Pushing the Boundaries of Crowd-enabled Databases with Query-driven Schema Expansion | 2012 | VLDB | 4.703047e-05 |
| 94 | CrowdDB: Answering Queries with Crowdsourcing | 2011 | SIGMOD | 0.00051273089 |
| 11,993 | Online Ordering of Overlapping Data Sources | 2014 | VLDB | 4.1905499e-05 |
| 8,691 | CrowdMiner: Mining association rules from the crowd | 2013 | VLDB | 4.4618888e-05 |
| 3,939 | CrowdQ: Crowdsourced Query Understanding | 2013 | CIDR | 6.6099823e-05 |
| 11,920 | NL2CM: A Natural Language Interface to Crowd Mining | 2015 | SIGMOD | 4.1905499e-05 |
| 3,104 | Crowd Mining | 2013 | SIGMOD | 7.5577057e-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 |