MAST: Towards Efficient Analytical Query Processing on Point Cloud Data
Summary: MAST enables approximate analytics on point clouds by sampling core frames under a budget to minimize DL calls. It fuses multi-agent RL sampling with a spatio-temporal index to accelerate PC retrieval and aggregates, with provable error bounds. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Jiangneng Li (Nanyang Technological University)
- 2. Haitao Yuan (Nanyang Technological University)
- 3. Gao Cong (Nanyang Technological University)
- 4. Han Mao Kiah (Nanyang Technological University)
- 5. Shuhao Zhang (Huazhong University of Science and Technology)
BibTeX Citation
@inproceedings{li_sigmod25,
title = {{MAST: Towards Efficient Analytical Query Processing on Point Cloud Data}},
author = {Li, Jiangneng and Yuan, Haitao and Cong, Gao and Kiah, Han Mao and Zhang, Shuhao},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3709702},
url = {https://dl.acm.org/doi/10.1145/3709702},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,159 | Demonstrating MAST: An Efficient System for Point Cloud Data Analytics | 2025 | SIGMOD | 4.9769913e-05 |
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