ST4ML: Machine Learning Oriented Spatio-Temporal Data Processing at Scale
Summary: ST4ML enables scalable ML-focused spatio-temporal analytics via a 3-stage pipeline (selection-conversion-extraction) on Spark. First ML-centric ST analytics; ST4ML yields up to 10x speedups on real data and is open-source at github.com/Panrong/st4ml. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Kaiqi Liu (Alibaba; Nanyang Technological University)
- 2. Panrong Tong (Alibaba; Nanyang Technological University)
- 3. Mo Li (Alibaba; Nanyang Technological University)
- 4. Yue Wu (Alibaba; Nanyang Technological University)
- 5. Jianqiang Huang (Alibaba; Nanyang Technological University)
BibTeX Citation
@inproceedings{liu_sigmod23,
title = {{ST4ML: Machine Learning Oriented Spatio-Temporal Data Processing at Scale}},
author = {Liu, Kaiqi and Tong, Panrong and Li, Mo and Wu, Yue and Huang, Jianqiang},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588941},
url = {https://dl.acm.org/doi/10.1145/3588941},
year = {2023}
}
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