Accelerating Approximate Analytical Join Queries over Unstructured Data with Statistical Guarantees
Summary: Blocking-augmented sampling (BaS) combines embedding blocking with sampling for ML-based joins over unstructured data. It adaptively handles false negatives/positives, providing confidence intervals and up to 19× lower error than prior methods. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Yuxuan Zhu (University of Illinois Urbana-Champaign)
- 2. Tengjun Jin (University of Illinois Urbana-Champaign)
- 3. Chenghao Mo (University of Illinois Urbana-Champaign)
- 4. Daniel Kang (University of Illinois Urbana-Champaign)
BibTeX Citation
@inproceedings{zhu_sigmod26,
title = {{Accelerating Approximate Analytical Join Queries over Unstructured Data with Statistical Guarantees}},
author = {Zhu, Yuxuan and Jin, Tengjun and Mo, Chenghao and Kang, Daniel},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802004},
url = {https://dl.acm.org/doi/10.1145/3802004},
year = {2026}
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