LANTERN: Boredom-conscious Natural Language Description Generation of Query Execution Plans for Database Education
Summary: LANTERN generates natural-language descriptions of query execution plans to aid database education. It offers POOL, a generic declarative framework for SMEs to author NL descriptions of physical operators, and combines rule-based and deep-learning techniques to diversify explanations and curb learner boredom. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Peng Chen
- 2. Hui Li
- 3. Sourav S Bhowmick
- 4. Shafiq R Joty
- 5. Weiguo Wang
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,780 | MOCHA: A Tool for Visualizing Impact of Operator Choices in Query Execution Plans for Database Education | 2022 | VLDB | 5.3298375e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 204 | Learned Cardinalities: Estimating Correlated Joins with Deep Learning | 2019 | CIDR | 0.00034784455 |
| 984 | Natural language to SQL: Where are we today? | 2020 | VLDB | 0.00014857465 |
| 8,990 | Towards Enhancing Database Education: Natural Language Generation Meets Query Execution Plans | 2021 | SIGMOD | 4.413295e-05 |
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