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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
- 6829
- Venue
- SIGMOD
- Year
- 2024
- Pagerank
- 4.1905499e-05
- Overall Rank
- 10,947 | 23.92%
- DOI
-
10.1145/3639274
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No non-self incoming citations found for this paper in this database.
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
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 |
| 510 |
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning |
2019 |
SIGMOD |
0.00021420477 |
| 694 |
BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics |
2020 |
VLDB |
0.00018031141 |
| 779 |
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning |
2019 |
VLDB |
0.00016719473 |
| 1,390 |
MIRIS: Fast Object Track Queries in Video |
2020 |
SIGMOD |
0.00012242018 |
| 3,291 |
Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics |
2021 |
VLDB |
7.2607192e-05 |
| 4,492 |
TASTI: Semantic Indexes for Machine Learning-based Queries over Unstructured Data |
2022 |
SIGMOD |
6.1374891e-05 |
| 4,565 |
Optimizing Video Analytics with Declarative Model Relationships |
2023 |
VLDB |
6.0746821e-05 |
| 4,703 |
Accelerating Approximate Aggregation Queries with Expensive Predicates |
2021 |
VLDB |
5.9793615e-05 |
| 4,865 |
OTIF: Efficient Tracker Pre-processing over Large Video Datasets |
2022 |
SIGMOD |
5.8627966e-05 |
| 5,168 |
FiGO: Fine-Grained Query Optimization in Video Analytics |
2022 |
SIGMOD |
5.6446115e-05 |
| 5,232 |
Zeus: Efficiently Localizing Actions in Videos using Reinforcement Learning |
2022 |
SIGMOD |
5.6094155e-05 |
| 6,184 |
Top-K Deep Video Analytics: A Probabilistic Approach |
2021 |
SIGMOD |
5.1636368e-05 |
| 7,922 |
Video-zilla: An Indexing Layer for Large-Scale Video Analytics |
2022 |
SIGMOD |
4.6114931e-05 |
| 9,750 |
Co-movement Pattern Mining from Videos |
2024 |
VLDB |
4.2856385e-05 |
| 9,770 |
DoveDB: A Declarative and Low-Latency Video Database |
2023 |
VLDB |
4.2815042e-05 |
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