PLM4NDV: Minimizing Data Access for Number of Distinct Values Estimation with Pre-trained Language Models
Summary: Leverages semantic schema via pre-trained language models to estimate the number of distinct values (NDV) with reduced data access. PLM4NDV fuses target-column and table semantics to lower access costs, can operate with no data access, and outperforms baselines on large real-world datasets. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Xianghong Xu (ByteDance)
- 2. Xiao He (ByteDance)
- 3. Tieying Zhang (ByteDance)
- 4. Lei Zhang (ByteDance)
- 5. Rui Shi (ByteDance)
- 6. Jianjun Chen (ByteDance)
BibTeX Citation
@inproceedings{xu_sigmod25,
title = {{PLM4NDV: Minimizing Data Access for Number of Distinct Values Estimation with Pre-trained Language Models}},
author = {Xu, Xianghong and He, Xiao and Zhang, Tieying and Zhang, Lei and Shi, Rui and Chen, Jianjun},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725336},
url = {https://dl.acm.org/doi/10.1145/3725336},
year = {2025}
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