Data-Agnostic Cardinality Learning from Imperfect Workloads
Summary: GRASP enables data-agnostic cardinality estimation from incomplete, imbalanced workloads without accessing underlying data. Compositional models generalize to unseen joins, handle range-distribution shifts, and capture cross-table correlations, matching data-driven methods with only 10% of templates. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Peizhi Wu (University of Pennsylvania)
- 2. Rong Kang (ByteDance)
- 3. Tieying Zhang (ByteDance)
- 4. Jianjun Chen (ByteDance)
- 5. Ryan Marcus (University of Pennsylvania)
- 6. Zachary G. Ives (University of Pennsylvania)
BibTeX Citation
@article{wu_vldb25,
title = {{Data-Agnostic Cardinality Learning from Imperfect Workloads}},
author = {Wu, Peizhi and Kang, Rong and Zhang, Tieying and Chen, Jianjun and Marcus, Ryan and Ives, Zachary G.},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {8},
pages = {2519--2532},
doi = {10.14778/3742728.3742745},
url = {https://doi.org/10.14778/3742728.3742745},
year = {2025}
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