Data Curation at Scale: The Data Tamer System
Summary: Introduces Data Tamer: an end-to-end, scalable data curation system that uses ML for attribute identification, schema/table grouping, transformations, and deduplication with human-in-the-loop visualization to assemble composites from sequences of sources. Evaluated on real enterprise workloads (up to tens of thousands of sources), showing ≈90% reduction in curation cost versus deployed production software. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Michael Stonebraker
- 2. Daniel Bruckner
- 3. Ihab F. Ilyas
- 4. George Beskales
- 5. Mitch Cherniack
- 6. Stan Zdonik
- 7. Alexander Pagan
- 8. Shan Xu
Incoming Citations (Sorted by Pagerank)
Showing 38 of 38 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 108 | WebTables: Exploring the Power of Tables on the Web | 2008 | VLDB | 0.00048345996 |
| 112 | Potter's Wheel: An Interactive Data Cleaning System | 2001 | VLDB | 0.00047010908 |
| 2,923 | Semi-Automatic Schema Integration in Clio | 2007 | VLDB | 7.8960974e-05 |
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