Beibin Li, Yao Lu, Srikanth Kandula. Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts. In Zachary Ives, Angela Bonifati, Amr El Abbadi, editors, SIGMOD '22: International Conference on Management of Data, Philadelphia, PA, USA, June 12 - 17, 2022. pages 1920-1933, ACM, 2022. [doi]
@inproceedings{LiLK22-5,
title = {Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts},
author = {Beibin Li and Yao Lu and Srikanth Kandula},
year = {2022},
doi = {10.1145/3514221.3526179},
url = {https://doi.org/10.1145/3514221.3526179},
researchr = {https://researchr.org/publication/LiLK22-5},
cites = {0},
citedby = {0},
pages = {1920-1933},
booktitle = {SIGMOD '22: International Conference on Management of Data, Philadelphia, PA, USA, June 12 - 17, 2022},
editor = {Zachary Ives and Angela Bonifati and Amr El Abbadi},
publisher = {ACM},
isbn = {978-1-4503-9249-5},
}