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
- 2. Haitao Yuan
- 3. Jie Wang
- 4. Ziting Wang
- 5. Han Mao Kiah
- 6. Gao Cong
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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 |
|---|---|---|---|---|
| 4,176 | Ganos: A Multidimensional, Dynamic, and Scene-Oriented Cloud-Native Spatial Database Engine | 2022 | VLDB | 6.3837225e-05 |
| 10,382 | MAST: Towards Efficient Analytical Query Processing on Point Cloud Data | 2025 | SIGMOD | 4.1945683e-05 |
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