Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts-Reference-Cited by-同舟云学术

Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts

Published:2022-06-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2022 International Conference on Management of Data
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Author:

Li Beibin¹,Lu Yao²,Kandula Srikanth³

Affiliation:

1. University of Washington, Seattle, WA, USA

2. Microsoft, Redmond, WA, USA

3. Microsoft, Remond, WA, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3514221.3526179

Reference50 articles.

1. 2022. TPC-H Benchmark. http://www.tpc.org/tpch/. 2022. TPC-H Benchmark. http://www.tpc.org/tpch/.

2. 2022. Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts - Extended Report. http://www.beibinli.com/docs/warper_extended_report.pdf 2022. Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts - Extended Report. http://www.beibinli.com/docs/warper_extended_report.pdf

3. Nicolas Bruno , Surajit Chaudhuri , and Luis Gravano . 2001 . STHoles: A Multi-dimensional Workload-aware Histogram . In Proceedings of the ACM SIGMOD International Conference on Management of Data. 211--222 . Nicolas Bruno, Surajit Chaudhuri, and Luis Gravano. 2001. STHoles: A Multi-dimensional Workload-aware Histogram. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 211--222.

4. An overview of query optimization in relational systems

5. Self-Ensembling With GAN-Based Data Augmentation for Domain Adaptation in Semantic Segmentation

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