Relational Query Synthesis ⋈ Decision Tree Learning
Summary: Interleaves search-based SPJ synthesis (for joins/projections) with decision-tree learning (for categorical and numeric comparison predicates) to get a complete, minimality-encouraging relational query synthesizer. LIBRA solves 1,361/1,475 benchmark instances (avg 59s) and outperforms Scythe and PatSQL. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Aaditya Naik
- 2. Aalok Thakkar
- 3. Adam Stein
- 4. Rajeev Alur
- 5. Mayur Naik
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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 |
|---|---|---|---|---|
| 903 | To Join or Not to Join? Thinking Twice about Joins before Feature Selection | 2016 | SIGMOD | 0.0001547016 |
| 984 | Natural language to SQL: Where are we today? | 2020 | VLDB | 0.00014857465 |
| 1,430 | Duoquest: A Dual-Specification System for Expressive SQL Queries | 2020 | SIGMOD | 0.00012031061 |
| 1,463 | ARDA: Automatic Relational Data Augmentation for Machine Learning | 2020 | VLDB | 0.00011869295 |
| 3,345 | QuickFOIL: Scalable Inductive Logic Programming | 2015 | VLDB | 7.1958815e-05 |
| 3,598 | The Relational Model Of Data And Cylindrical Algebras | 1982 | PODS | 6.9328438e-05 |
| 4,980 | Interactive Query Synthesis from Input-Output Examples | 2017 | SIGMOD | 5.7873101e-05 |
| 5,544 | PATSQL: Efficient Synthesis of SQL Queries from Example Tables with Quick Inference of Projected Columns | 2021 | VLDB | 5.4462933e-05 |
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