CEDA: Learned Cardinality Estimation with Domain Adaptation
Summary: CEDA synthesizes training workloads from the database distribution and integrates histogram-derived features into an attention-based learned cardinality estimator to boost accuracy. It then applies domain adaptation to robustly generalize to unlabeled, drifting workloads, avoiding costly label collection. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zilong Wang
- 2. Qixiong Zeng
- 3. Ning Wang
- 4. Haowen Lu
- 5. Yue Zhang
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,336 | Refactoring Index Tuning Process with Benefit Estimation | 2024 | VLDB | 4.7599411e-05 |
| 9,825 | Athena: An Effective Learning-based Framework for Query Optimizer Performance Improvement | 2025 | SIGMOD | 4.2751057e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 71 | How Good Are Query Optimizers, Really? | 2016 | VLDB | 0.00059038975 |
| 204 | Learned Cardinalities: Estimating Correlated Joins with Deep Learning | 2019 | CIDR | 0.00034784455 |
| 406 | Massive Stochastic Testing of SQL | 1998 | VLDB | 0.00024053686 |
| 758 | Deep Unsupervised Cardinality Estimation | 2020 | VLDB | 0.0001706608 |
| 1,254 | Selectivity Estimation for Range Predicates using Lightweight Models | 2019 | VLDB | 0.00013027411 |
| 3,449 | Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation | 2022 | VLDB | 7.0824319e-05 |
| 6,569 | Domain Adaptation for Deep Entity Resolution | 2022 | SIGMOD | 5.0065379e-05 |
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