Optimizing Video Queries with Declarative Clues

Summary: Introduces Clues: declarative user‑specified hints (types+instances with rewrite rules) that steer VDBMS optimizers to prune search and exploit domain knowledge. ClueVQS auto-applies Clues to trade accuracy for large speedups (up to 100×) and major optimizer time reductions under user accuracy bounds. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: h6ffe4eb5686896b7
Venue: VLDB
Year: 2024
Pagerank: 4.9793485e-05
Overall Rank: 11,596 | 22.04%
DOI: 10.14778/3681954.3681998

Incoming Non-self Citations Over Time

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

Authors

1. Daren Chao (University of Toronto)
2. Yueting Chen (York University)
3. Nick Koudas (University of Toronto)
4. Xiaohui Yu (York University)

BibTeX Citation

@article{chao_vldb24,
        title = {{Optimizing Video Queries with Declarative Clues}},
        author = {Chao, Daren and Chen, Yueting and Koudas, Nick and Yu, Xiaohui},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {11},
        pages = {3256--3268},
        doi = {10.14778/3681954.3681998},
        url = {https://doi.org/10.14778/3681954.3681998},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 18 of 18 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
281	Accelerating Machine Learning Inference with Probabilistic Predicates	2018	SIGMOD	0.00022295232
541	BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics	2020	VLDB	0.00016657685
896	Froid: Optimization of Imperative Programs in a Relational Database	2018	VLDB	0.00013209291
1,024	MIRIS: Fast Object Track Queries in Video	2020	SIGMOD	0.00012430491
1,617	Challenges and Opportunities in DNN-Based Video Analytics: A Demonstration of the BlazeIt Video Query Engine	2019	CIDR	0.00010057464
1,893	WeTune: Automatic Discovery and Verification of Query Rewrite Rules	2022	SIGMOD	9.4126198e-05
2,787	EVA: A Symbolic Approach to Accelerating Exploratory Video Analytics with Materialized Views	2022	SIGMOD	8.0158999e-05
3,429	SVQ: Streaming Video Queries	2019	SIGMOD	7.306047e-05
3,650	TASTI: Semantic Indexes for Machine Learning-based Queries over Unstructured Data	2022	SIGMOD	7.1341771e-05
3,778	Optimizing Video Analytics with Declarative Model Relationships	2023	VLDB	7.0259119e-05
3,874	Accelerating Approximate Aggregation Queries with Expensive Predicates	2021	VLDB	6.953738e-05
3,948	OTIF: Efficient Tracker Pre-processing over Large Video Datasets	2022	SIGMOD	6.9053923e-05
3,969	Optimizing Machine Learning Inference Queries with Correlative Proxy Models	2022	VLDB	6.8899861e-05
4,286	SVQ++: Querying for Object Interactions in Video Streams	2020	SIGMOD	6.6873151e-05
4,495	Evaluating Temporal Queries Over Video Feeds	2021	SIGMOD	6.5769374e-05
4,775	Zeus: Efficiently Localizing Actions in Videos using Reinforcement Learning	2022	SIGMOD	6.4193428e-05
5,211	Seiden: Revisiting Query Processing in Video Database Systems	2023	VLDB	6.2253642e-05
8,410	TQVS: Temporal Queries over Video Streams in Action	2020	SIGMOD	5.3365631e-05

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