TATA: An Efficient Framework for Task Transfer in Query Plan Representation
Summary: TATA: a task-transfer framework for query-plan representations that augments pretraining with a self-supervised plan decoder to produce robust, task-agnostic embeddings. Mitigates small-label and distribution-shift issues by synthesizing realistic pseudo-labeled plans using DB domain knowledge, yielding up to 5x reduction in dataset collection cost and working across multiple plan encoders. (summarized by gpt-5-mini on Mar 13 2026)
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Authors
- 1. Yue Zhao (Nanyang Technological University)
- 2. Songsong Mo (Nanyang Technological University)
- 3. Gao Cong (Nanyang Technological University)
BibTeX Citation
@article{zhao_vldb26,
title = {{TATA: An Efficient Framework for Task Transfer in Query Plan Representation}},
author = {Zhao, Yue and Mo, Songsong and Cong, Gao},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {3},
pages = {413--425},
doi = {10.14778/3778092.3778102},
url = {https://doi.org/10.14778/3778092.3778102},
year = {2026}
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