High-Throughput Ingestion for Video Warehouse: Comprehensive Configuration and Effective Exploration
Summary: Hippo enables real-time video ingestion via V-ETL, reframing analytics as a data-warehouse task for hundreds of streams. It defines a 1e7x larger configuration space, accuracy-aware search with graph embeddings and RL, plus clustering with MILP to maximize accuracy under latency, delivering 300 streams with >30% gains. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Baiyan Zhang (Zhejiang University)
- 2. Zepeng Li (Zhejiang University)
- 3. Dongxiang Zhang (Zhejiang University)
- 4. Huan Li (Zhejiang University)
- 5. Kian-Lee Tan (National University of Singapore)
- 6. Gang Chen (Zhejiang University)
BibTeX Citation
@inproceedings{zhang_sigmod25,
title = {{High-Throughput Ingestion for Video Warehouse: Comprehensive Configuration and Effective Exploration}},
author = {Zhang, Baiyan and Li, Zepeng and Zhang, Dongxiang and Li, Huan and Tan, Kian-Lee and Chen, Gang},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725407},
url = {https://dl.acm.org/doi/10.1145/3725407},
year = {2025}
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,589 | Query-Aware Path Inference from Spatial Videos | 2026 | SIGMOD | 4.9793485e-05 |
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