Predictive and Near-Optimal Sampling for View Materialization in Video Databases
Summary: LEAP enables predictive MOT-based view materialization in video databases using lower-bound frame-sampling theory, a data-driven motion predictor, and a cross-frame associator. Across seven datasets, it achieves up to 9x fewer frames and 5x faster queries, enabling real-time throughput for 160 streams on a single RTX 3090Ti. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Yanchao Xu (Zhejiang University)
- 2. Dongxiang Zhang (Zhejiang University)
- 3. Shuhao Zhang (Nanyang Technological University)
- 4. Sai Wu (Zhejiang University)
- 5. Zexu Feng (Zhejiang University)
- 6. Gang Chen (Zhejiang University)
BibTeX Citation
@inproceedings{xu_sigmod24,
title = {{Predictive and Near-Optimal Sampling for View Materialization in Video Databases}},
author = {Xu, Yanchao and Zhang, Dongxiang and Zhang, Shuhao and Wu, Sai and Feng, Zexu and Chen, Gang},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3639274},
url = {https://dl.acm.org/doi/10.1145/3639274},
year = {2024}
}
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,393 | Query-Aware Path Inference from Spatial Videos | 2026 | SIGMOD | 5.093636e-05 |
| 10,791 | High-Throughput Ingestion for Video Warehouse: Comprehensive Configuration and Effective Exploration | 2025 | SIGMOD | 5.093636e-05 |
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