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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)

Paper ID
13421
Venue
VLDB
Year
2023
Pagerank
5.2626014e-05
Overall Rank
9,491 | 34.89%
DOI
10.14778/3611540.3611589

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{wang_vldb23,
        title = {{CEDA: Learned Cardinality Estimation with Domain Adaptation}},
        author = {Wang, Zilong and Zeng, Qixiong and Wang, Ning and Lu, Haowen and Zhang, Yue},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {12},
        pages = {3934--3937},
        doi = {10.14778/3611540.3611589},
        url = {https://doi.org/10.14778/3611540.3611589},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
7,076 Refactoring Index Tuning Process with Benefit Estimation 2024 VLDB 5.7098893e-05
9,971 Athena: An Effective Learning-based Framework for Query Optimizer Performance Improvement 2025 SIGMOD 5.1845938e-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.

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