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 |
|---|---|---|---|---|
| 894 | A Hybrid Approach to Functional Dependency Discovery | 2016 | SIGMOD | 0.00015556428 |
| 1,625 | Data Profiling with Metanome | 2015 | VLDB | 0.00011094926 |
| 1,908 | Information-Theoretic Tools for Mining Database Structure from Large Data Sets | 2004 | SIGMOD | 0.00010126101 |
| 2,574 | Discovery of Genuine Functional Dependencies from Relational Data with Missing Values | 2018 | VLDB | 8.5173637e-05 |
| 3,745 | DeepSqueeze: Deep Semantic Compression for Tabular Data | 2020 | SIGMOD | 6.7926132e-05 |
| 3,787 | White-box Compression: Learning and Exploiting Compact Table Representations | 2020 | CIDR | 6.7674374e-05 |
| 4,127 | A Statistical Perspective on Discovering Functional Dependencies in Noisy Data | 2020 | SIGMOD | 6.4310458e-05 |
| 7,076 | Mining Approximate Acyclic Schemes from Relations | 2020 | SIGMOD | 4.8426354e-05 |
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