Optimizing Video Analytics with Declarative Model Relationships
Summary: Introduces declarative CAN REPLACE/CAN FILTER hints for expressing relationships among video ML models. VIVA validates hints and explores transformed SQL plans to meet accuracy constraints, achieving up to 16.6× speedups without accuracy loss. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Francisco Romero (Stanford University)
- 2. Johann Hauswald (Stanford University; Sutter Hill Ventures)
- 3. Aditi Partap (Stanford University)
- 4. Daniel Kang (Stanford University)
- 5. Matei Zaharia (Stanford University)
- 6. Christos Kozyrakis (Stanford University)
BibTeX Citation
@article{romero_vldb23,
title = {{Optimizing Video Analytics with Declarative Model Relationships}},
author = {Romero, Francisco and Hauswald, Johann and Partap, Aditi and Kang, Daniel and Zaharia, Matei and Kozyrakis, Christos},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {3},
pages = {447--460},
doi = {10.14778/3570690.3570695},
url = {https://doi.org/10.14778/3570690.3570695},
year = {2023}
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