Opening The Black-Box: Explaining Learned Cost Models For Databases
Summary: First application of AI explainability to learned query cost models: adapted feature-attribution and saliency methods to make deep LCMs interpretable. Demo interactive tool to diagnose tail prediction errors and guide model fixes. (summarized by gpt-5-mini on Feb 09 2026)
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Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,844 | Learned Cost Models for Query Optimization: From Batch to Streaming Systems | 2025 | VLDB | 5.1725247e-05 |
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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 |
|---|---|---|---|---|
| 86 | Learned Cardinalities: Estimating Correlated Joins with Deep Learning | 2019 | CIDR | 0.0003577267 |
| 560 | Plan-Structured Deep Neural Network Models for Query Performance Prediction | 2019 | VLDB | 0.00016570735 |
| 2,929 | Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction | 2022 | VLDB | 8.0220847e-05 |
| 6,282 | How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks | 2025 | SIGMOD | 5.9906069e-05 |
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