A Unified Transferable Model for ML-Enhanced DBMS
Summary: MTMLF: unified transferable ML for DBMS, using multi-task training to capture cross-task signals and pretrain–fine-tune to distill meta-knowledge across databases, eliminating expensive per-DB retraining and large new-data needs. Demonstrated for query optimization. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Ziniu Wu (Massachusetts Institute of Technology)
- 2. Pei Yu (Alibaba; University of Science and Technology Beijing)
- 3. Peilun Yang (Alibaba; University of Technology Sydney)
- 4. Rong Zhu (Alibaba)
- 5. Yuxing Han (Alibaba)
- 6. Yaliang Li (Alibaba)
- 7. Defu Lian (University of Science and Technology Beijing)
- 8. Kai Zeng (Alibaba)
- 9. Jingren Zhou (Alibaba)
BibTeX Citation
@inproceedings{wu_cidr22,
address = {Amsterdam, Netherlands},
series = {{CIDR} '22},
title = {{A Unified Transferable Model for ML-Enhanced DBMS}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Wu, Ziniu and Yu, Pei and Yang, Peilun and Zhu, Rong and Han, Yuxing and Li, Yaliang and Lian, Defu and Zeng, Kai and Zhou, Jingren},
year = {2022}
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