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 (Databricks)
- 2. Daniel Ting (Tableau Research)
BibTeX Citation
@inproceedings{talbot_sigmod22,
title = {{Statistical Schema Learning using Occam's Razor}},
author = {Talbot, Justin and Ting, Daniel},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3526174},
url = {https://dl.acm.org/doi/10.1145/3514221.3526174},
year = {2022}
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 618 | A Hybrid Approach to Functional Dependency Discovery | 2016 | SIGMOD | 0.00015711835 |
| 1,374 | Data Profiling with Metanome | 2015 | VLDB | 0.00010986078 |
| 1,913 | Information-Theoretic Tools for Mining Database Structure from Large Data Sets | 2004 | SIGMOD | 9.4928569e-05 |
| 2,123 | Discovery of Genuine Functional Dependencies from Relational Data with Missing Values | 2018 | VLDB | 9.1372798e-05 |
| 3,092 | DeepSqueeze: Deep Semantic Compression for Tabular Data | 2020 | SIGMOD | 7.7679406e-05 |
| 3,336 | White-box Compression: Learning and Exploiting Compact Table Representations | 2020 | CIDR | 7.5084986e-05 |
| 3,441 | A Statistical Perspective on Discovering Functional Dependencies in Noisy Data | 2020 | SIGMOD | 7.4138323e-05 |
| 6,929 | Mining Approximate Acyclic Schemes from Relations | 2020 | SIGMOD | 5.7369354e-05 |
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