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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)

Paper ID
h6565643426c8ff25
Venue
SIGMOD
Year
2024
Pagerank
4.9605343e-05
Overall Rank
11,566 | 22.48%
DOI
10.1145/3639274

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

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)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
10,662 Query-Aware Path Inference from Spatial Videos 2026 SIGMOD 4.9605343e-05
11,273 High-Throughput Ingestion for Video Warehouse: Comprehensive Configuration and Effective Exploration 2025 SIGMOD 4.9605343e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 15 of 15 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
309 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.0002143249
411 QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning 2019 VLDB 0.0001874043
541 BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics 2020 VLDB 0.00016627384
1,023 MIRIS: Fast Object Track Queries in Video 2020 SIGMOD 0.00012398361
3,224 Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics 2021 VLDB 7.503511e-05
3,629 TASTI: Semantic Indexes for Machine Learning-based Queries over Unstructured Data 2022 SIGMOD 7.1286458e-05
3,747 Accelerating Approximate Aggregation Queries with Expensive Predicates 2021 VLDB 7.0351475e-05
3,782 Optimizing Video Analytics with Declarative Model Relationships 2023 VLDB 7.0029633e-05
3,889 FiGO: Fine-Grained Query Optimization in Video Analytics 2022 SIGMOD 6.9269184e-05
3,957 OTIF: Efficient Tracker Pre-processing over Large Video Datasets 2022 SIGMOD 6.8827279e-05
4,795 Zeus: Efficiently Localizing Actions in Videos using Reinforcement Learning 2022 SIGMOD 6.3991535e-05
5,812 Top-K Deep Video Analytics: A Probabilistic Approach 2021 SIGMOD 5.9697648e-05
7,015 Video-zilla: An Indexing Layer for Large-Scale Video Analytics 2022 SIGMOD 5.6044008e-05
9,576 DoveDB: A Declarative and Low-Latency Video Database 2023 VLDB 5.1424205e-05
10,120 Co-movement Pattern Mining from Videos 2024 VLDB 5.0638787e-05
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