Statistical Schema Learning using Occam's Razor
Summary: Unsupervised schema learning for denormalized tables via Occam's razor; learns an optimal schema from data instead of canonical normalization. Principled, noise-robust objective with user-specified properties; efficient learning algorithm, 3–100x faster than prior work, and 1/5th the errors. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Justin Talbot
- 2. Daniel Ting
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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 |
|---|---|---|---|---|
| 890 | A Hybrid Approach to Functional Dependency Discovery | 2016 | SIGMOD | 0.00015542177 |
| 1,627 | Data Profiling with Metanome | 2015 | VLDB | 0.0001108421 |
| 1,912 | Information-Theoretic Tools for Mining Database Structure from Large Data Sets | 2004 | SIGMOD | 0.00010116822 |
| 2,576 | Discovery of Genuine Functional Dependencies from Relational Data with Missing Values | 2018 | VLDB | 8.509121e-05 |
| 3,741 | DeepSqueeze: Deep Semantic Compression for Tabular Data | 2020 | SIGMOD | 6.7952067e-05 |
| 3,757 | White-box Compression: Learning and Exploiting Compact Table Representations | 2020 | CIDR | 6.7804933e-05 |
| 4,129 | A Statistical Perspective on Discovering Functional Dependencies in Noisy Data | 2020 | SIGMOD | 6.4208557e-05 |
| 7,073 | Mining Approximate Acyclic Schemes from Relations | 2020 | SIGMOD | 4.8378353e-05 |
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