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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
4.9769913e-05
Overall Rank
11,515 | 22.61%
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
10,600 Query-Aware Path Inference from Spatial Videos 2026 SIGMOD 4.9769913e-05
11,216 High-Throughput Ingestion for Video Warehouse: Comprehensive Configuration and Effective Exploration 2025 SIGMOD 4.9769913e-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
314 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.00021276452
437 QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning 2019 VLDB 0.00018310278
541 BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics 2020 VLDB 0.00016663833
1,023 MIRIS: Fast Object Track Queries in Video 2020 SIGMOD 0.00012430702
3,220 Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics 2021 VLDB 7.5172795e-05
3,640 TASTI: Semantic Indexes for Machine Learning-based Queries over Unstructured Data 2022 SIGMOD 7.1410511e-05
3,756 Accelerating Approximate Aggregation Queries with Expensive Predicates 2021 VLDB 7.0456217e-05
3,780 Optimizing Video Analytics with Declarative Model Relationships 2023 VLDB 7.0225859e-05
3,877 FiGO: Fine-Grained Query Optimization in Video Analytics 2022 SIGMOD 6.9496983e-05
3,947 OTIF: Efficient Tracker Pre-processing over Large Video Datasets 2022 SIGMOD 6.9052646e-05
4,778 Zeus: Efficiently Localizing Actions in Videos using Reinforcement Learning 2022 SIGMOD 6.4165071e-05
5,805 Top-K Deep Video Analytics: A Probabilistic Approach 2021 SIGMOD 5.9861675e-05
7,010 Video-zilla: An Indexing Layer for Large-Scale Video Analytics 2022 SIGMOD 5.6195953e-05
9,550 DoveDB: A Declarative and Low-Latency Video Database 2023 VLDB 5.159481e-05
10,086 Co-movement Pattern Mining from Videos 2024 VLDB 5.0806786e-05
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