Schuyler: Self-Supervised Clustering of Tables in Relational Databases
Summary: Schuyler clusters relational tables by combining schema/structural and semantic signals, fine-tuning a large language model with self-supervised triplet loss to produce embeddings with no labeled data. On a new five-DB benchmark (29–481 tables, 3–47 clusters) it improves prior art by +0.13 ARI and +0.10 AMI. (summarized by gpt-5-mini on Mar 13 2026)
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Authors
- 1. Lukas Laskowski
- 2. Fabian Panse
- 3. Michael Hladik
- 4. Jan Portisch
- 5. Felix Naumann
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Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 263 | OPTICS: Ordering Points To Identify the Clustering Structure | 1999 | SIGMOD | 0.00029955858 |
| 1,506 | Summarizing Relational Databases | 2009 | VLDB | 0.0001159691 |
| 3,429 | Discovering Topical Structures of Databases | 2008 | SIGMOD | 7.0996766e-05 |
| 3,539 | General purpose database summarization | 2005 | VLDB | 6.9925094e-05 |
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