ICARUS: Minimizing Human Effort in Iterative Data Completion
Summary: ICARUS reduces expert labor by presenting small, high-impact matrix subsets for edits to the matrix. Schema-informed hierarchies amplify edits into rules; heuristic subset selection yields ~50% improvement, with users filling 68% in an hour. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Protiva Rahman
- 2. Courtney Hebert
- 3. Arnab Nandi
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,358 | Adaptive Rule Discovery for Labeling Text Data | 2021 | SIGMOD | 5.5507062e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 18 of 18 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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