Demonstrating MAST: An Efficient System for Point Cloud Data Analytics
Summary: MAST is an efficient prototype for point cloud analytics, combining semantic and spatial predicates to enable accurate analytical queries. Demonstration shows the full stack (storage, preprocessing, query processing, visualization) and two real PC analytics queries. (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. Jie Wang (Nanyang Technological University)
- 4. Ziting Wang (Nanyang Technological University)
- 5. Han Mao Kiah (Nanyang Technological University)
- 6. Gao Cong (Nanyang Technological University)
BibTeX Citation
@inproceedings{li_sigmod25,
title = {{Demonstrating MAST: An Efficient System for Point Cloud Data Analytics}},
author = {Li, Jiangneng and Yuan, Haitao and Wang, Jie and Wang, Ziting and Kiah, Han Mao and Cong, Gao},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3722212.3725099},
url = {https://dl.acm.org/doi/10.1145/3722212.3725099},
year = {2025}
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,894 | Ganos: A Multidimensional, Dynamic, and Scene-Oriented Cloud-Native Spatial Database Engine | 2022 | VLDB | 6.0486927e-05 |
| 10,670 | MAST: Towards Efficient Analytical Query Processing on Point Cloud Data | 2025 | SIGMOD | 5.093636e-05 |
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