Optimizing Video Selection LIMIT Queries With Commonsense Knowledge

Summary: Paine accelerates video-selection LIMIT queries by using commonsense-derived probabilistic models to compensate for intentionally lossy indexes and prioritize relevant videos. It approaches full-index performance at lossy-index construction cost, processing up to 97.79% fewer videos. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 13602
Venue: VLDB
Year: 2024
Pagerank: 5.4065627e-05
Overall Rank: 8,589 | 41.08%
DOI: 10.14778/3654621.3654639

Incoming Non-self Citations Over Time

Authors

1. Wenjia He (University of Michigan)
2. Ibrahim Sabek (University of Southern California)
3. Yuze Lou (University of Michigan)
4. Michael Cafarella (Massachusetts Institute of Technology)

BibTeX Citation

@article{he_vldb24,
        title = {{Optimizing Video Selection LIMIT Queries With Commonsense Knowledge}},
        author = {He, Wenjia and Sabek, Ibrahim and Lou, Yuze and Cafarella, Michael},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {7},
        pages = {1751--1764},
        doi = {10.14778/3654621.3654639},
        url = {https://doi.org/10.14778/3654621.3654639},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,670	MAST: Towards Efficient Analytical Query Processing on Point Cloud Data	2025	SIGMOD	5.093636e-05
10,751	Approximating Opaque Top-k Queries	2025	SIGMOD	5.093636e-05
10,795	Scalable Complex Event Processing on Video Streams	2025	SIGMOD	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
24	Spark SQL: Relational Data Processing in Spark	2015	SIGMOD	0.00054865648
43	The Case for Learned Index Structures	2018	SIGMOD	0.00046060254
259	Database Cracking	2007	CIDR	0.00023119313
284	NoScope: Optimizing Neural Network Queries over Video at Scale	2017	VLDB	0.00022370521
295	Accelerating Machine Learning Inference with Probabilistic Predicates	2018	SIGMOD	0.00022238183
430	A Fast Index for Semistructured Data	2001	VLDB	0.00018600166
569	BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics	2020	VLDB	0.00016348191
1,042	MIRIS: Fast Object Track Queries in Video	2020	SIGMOD	0.00012451966
2,933	EVA: A Symbolic Approach to Accelerating Exploratory Video Analytics with Materialized Views	2022	SIGMOD	7.9474026e-05
3,253	Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics	2021	VLDB	7.5936939e-05
4,264	A Method for Optimizing Opaque Filter Queries	2020	SIGMOD	6.7937529e-05
6,494	Extract-Transform-Load for Video Streams	2023	VLDB	5.8639535e-05
9,238	Challenges and Techniques for Effective and Efficient Similarity Search in Large Video Databases	2008	VLDB	5.3015807e-05
9,406	Controlled Intentional Degradation in Analytical Video Systems	2022	SIGMOD	5.2751448e-05

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