Optimizing Video Queries with Declarative Clues
Bibliographic record
Abstract
Video Database Management Systems (VDBMS) leverage advancements in computer vision and deep learning for efficient video data analysis and retrieval. This paper introduces the concept of user-specified Clues, allowing users to incorporate domain-specific knowledge, referred to as Clues, into query optimization. Clues are expressed as Clue types, each associated with optimization rules, and applied to queries through Clue instances. The extensible ClueVQS system we present to incorporate these ideas, optimizes queries automatically, utilizing Clues to improve processing efficiency. We also introduce algorithms to optimize queries using Clues allowing for trade-offs between speed and query accuracy. Our proposals and system address challenges such as data-dependent Clue effectiveness, limiting search space, and accuracy-efficiency trade-offs. Detailed experimental results demonstrate query speedups of up to two orders of magnitude compared to other applicable approaches, and a reduction of the query optimizer time by up to 95% while respecting user-specified accuracy constraints, showcasing the effectiveness of the proposed framework.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".