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DeepVQL: Deep Video Queries on PostgreSQL

Summary: DeepVQL extends PostgreSQL with declarative video-database functions and UDFs for deep-learning-based object detection, tracking, and analytics. Its distinctive capability is querying moving objects under explicit spatial-region and temporal-duration predicates in traffic videos. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
13414
Venue
VLDB
Year
2023
Pagerank
5.4119882e-05
Overall Rank
8,518 | 41.56%
DOI
10.14778/3611540.3611583

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{lew_vldb23,
        title = {{DeepVQL: Deep Video Queries on PostgreSQL}},
        author = {Lew, Dong June and Yoo, Kihyun and Nam, Kwang Woo},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {12},
        pages = {3910--3913},
        doi = {10.14778/3611540.3611583},
        url = {https://doi.org/10.14778/3611540.3611583},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,393 Query-Aware Path Inference from Spatial Videos 2026 SIGMOD 5.093636e-05
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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.

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