Optimizing Block Skipping for High-Dimensional Data with Learned Adaptive Curve
Summary: Learned adaptive curve for SMA block skipping in high-dimensional data via an attention-based network and end-to-end training. Scales to 1000 columns with a 2.8x block-skipping improvement over static space-filling curves on real Spark workloads. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Xu Chen (University of Electronic Science and Technology of China)
- 2. Shuncheng Liu (Huawei)
- 3. Tong Yuan (Huawei)
- 4. Tao Ye (Huawei)
- 5. Kai Zeng (Huawei)
- 6. Han Su (University of Electronic Science and Technology of China; Yangtze Delta Region Institute (Quzhou))
- 7. Kai Zheng (Shenzhen University; University of Electronic Science and Technology of China)
BibTeX Citation
@inproceedings{chen_sigmod25,
title = {{Optimizing Block Skipping for High-Dimensional Data with Learned Adaptive Curve}},
author = {Chen, Xu and Liu, Shuncheng and Yuan, Tong and Ye, Tao and Zeng, Kai and Su, Han and Zheng, Kai},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3709710},
url = {https://dl.acm.org/doi/10.1145/3709710},
year = {2025}
}
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