Learning Efficiently Over Heterogeneous Databases
Summary: CastorX extends relational learning to heterogeneous databases with matching dependencies for schema alignment and similarity-based matches. Sampling sharpens learning efficiency while yielding accurate Datalog definitions of the target relation. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Jose Picado
- 2. Arash Termehchy
- 3. Sudhanshu Pathak
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Outgoing Citations (Sorted by Pagerank)
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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 |
|---|---|---|---|---|
| 702 | Reasoning about Record Matching Rules | 2009 | VLDB | 0.00017918203 |
| 903 | To Join or Not to Join? Thinking Twice about Joins before Feature Selection | 2016 | SIGMOD | 0.0001547016 |
| 7,664 | Schema Independent Relational Learning | 2017 | SIGMOD | 4.6857329e-05 |
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