NEURON: Query Execution Plan Meets Natural Language Processing For Augmenting DB Education
Summary: NEURON turns a DBMS query execution plan into plain-language, text-and-voice explanations for education (world's first). It also enables NLQA over QEPs, delivering interactive, expert-level insight into optimization strategies such as joins, nesting, and aggregation. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Siyuan Liu
- 2. Sourav S Bhowmick
- 3. Wanlu Zhang
- 4. Shu Wang
- 5. Wanyi Huang
- 6. Shafiq Joty
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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 |
|---|---|---|---|---|
| 206 | Constructing an Interactive Natural Language Interface for Relational Databases | 2015 | VLDB | 0.00034667032 |
| 535 | ATHENA: An Ontology-Driven System for Natural Language Querying over Relational Data Stores | 2016 | VLDB | 0.00020727678 |
| 567 | NaLIR: An Interactive Natural Language Interface for Querying Relational Databases | 2014 | SIGMOD | 0.00019966681 |
| 1,673 | λόγος: A System for Translating Queries into Narratives | 2012 | SIGMOD | 0.00010948717 |
| 2,816 | DBPal: A Learned NL-Interface for Databases | 2018 | SIGMOD | 8.0694738e-05 |
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