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Optimizing Video Selection LIMIT Queries With Commonsense Knowledge

Summary: Introduces Paine: a cheap, lossy-index plus probabilistic commonsense models that patch missed targets and prioritize predicate-related videos for LIMIT video-selection queries. Cuts expensive detector scans dramatically—up to 97.8% fewer videos; even zero-content commonsense models give ~75% improvement while keeping index construction costs low. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13414
Venue
VLDB
Year
2024
Pagerank
4.4710897e-05
Overall Rank
8,672 | 39.68%
DOI
10.14778/3654621.3654639

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Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
10,382 MAST: Towards Efficient Analytical Query Processing on Point Cloud Data 2025 SIGMOD 4.1945683e-05
10,471 Approximating Opaque Top-k Queries 2025 SIGMOD 4.1945683e-05
10,523 Scalable Complex Event Processing on Video Streams 2025 SIGMOD 4.1945683e-05
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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.

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