ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads
Summary: ALECE uses attention over learned data aggregations and query representations to capture attribute correlations for SPJ cardinality estimation under dynamic workloads. Integrated with PostgreSQL, it substantially outperforms traditional and learned estimators, approaching optimal plan performance. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Pengfei Li (Alibaba)
- 2. Wenqing Wei (Alibaba)
- 3. Rong Zhu (Alibaba)
- 4. Bolin Ding (Alibaba)
- 5. Jingren Zhou (Alibaba)
- 6. Hua Lu (Roskilde University)
BibTeX Citation
@article{li_vldb24,
title = {{ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads}},
author = {Li, Pengfei and Wei, Wenqing and Zhu, Rong and Ding, Bolin and Zhou, Jingren and Lu, Hua},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {2},
pages = {197--210},
doi = {10.14778/3626292.3626302},
url = {https://doi.org/10.14778/3626292.3626302},
year = {2024}
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