DoveDB: A Declarative and Low-Latency Video Database
Summary: DoveDB introduces VMQL, a declarative language unifying video-model training, deployment, tracking, and analytics. Lightweight tracklet ingestion and semantic indexes enable low-latency aggregation/top-k queries across large camera networks. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ziyang Xiao (Zhejiang University)
- 2. Dongxiang Zhang (Zhejiang University)
- 3. Zepeng Li (Zhejiang University)
- 4. Sai Wu (Zhejiang University)
- 5. Kian-Lee Tan (National University of Singapore)
- 6. Gang Chen (Zhejiang University)
BibTeX Citation
@article{xiao_vldb23,
title = {{DoveDB: A Declarative and Low-Latency Video Database}},
author = {Xiao, Ziyang and Zhang, Dongxiang and Li, Zepeng and Wu, Sai and Tan, Kian-Lee and Chen, Gang},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {12},
pages = {3906--3909},
doi = {10.14778/3611540.3611582},
url = {https://doi.org/10.14778/3611540.3611582},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,589 | Query-Aware Path Inference from Spatial Videos | 2026 | SIGMOD | 4.9793485e-05 |
| 10,858 | Sema: A High-performance System for LLM-based Semantic Query Processing | 2026 | VLDB | 4.9793485e-05 |
| 11,310 | Deja Vu: Efficient Video-Language Query Engine with Learning-based Inter-Frame Computation Reuse | 2025 | VLDB | 4.9793485e-05 |
| 11,509 | Predictive and Near-Optimal Sampling for View Materialization in Video Databases | 2024 | SIGMOD | 4.9793485e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,024 | MIRIS: Fast Object Track Queries in Video | 2020 | SIGMOD | 0.00012430491 |
| 3,806 | Vaas: Video Analytics At Scale | 2020 | VLDB | 7.0107906e-05 |
| 3,948 | OTIF: Efficient Tracker Pre-processing over Large Video Datasets | 2022 | SIGMOD | 6.9053923e-05 |
| 3,950 | Visual Road: A Video Data Management Benchmark | 2019 | SIGMOD | 6.9050253e-05 |
| 4,286 | SVQ++: Querying for Object Interactions in Video Streams | 2020 | SIGMOD | 6.6873151e-05 |
| 8,616 | Query-Driven Video Event Processing for the Internet of Multimedia Things | 2021 | VLDB | 5.3019687e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 5,166 | LightDB: A DBMS for Virtual Reality Video | 2018 | VLDB |
| 2 | 3,950 | Visual Road: A Video Data Management Benchmark | 2019 | SIGMOD |
| 3 | 10,839 | Craw: A Unified and Efficient Querying Framework for Large-Scale Video Datasets | 2026 | VLDB |
| 4 | 9,602 | SketchQL Demonstration: Zero-shot Video Moment Querying with Sketches | 2024 | VLDB |
| 5 | 3,429 | SVQ: Streaming Video Queries | 2019 | SIGMOD |
| 6 | 13,773 | Demonstration of Apperception: A Database Management System for Geospatial Video Data | 2021 | VLDB |
| 7 | 9,589 | SketchQL: Video Moment Querying with a Visual Query Interface | 2024 | SIGMOD |
| 8 | 11,596 | Optimizing Video Queries with Declarative Clues | 2024 | VLDB |
| 9 | 5,026 | VisualWorldDB: A DBMS for the Visual World | 2020 | CIDR |
| 10 | 8,686 | DeepVQL: Deep Video Queries on PostgreSQL | 2023 | VLDB |