FiGO: Fine-Grained Query Optimization in Video Analytics
Summary: FiGO enables fine-grained query optimization in video analytics with a throughput–accuracy ensemble, assigning chunks to suitable models to meet accuracy. It prunes the ensemble to cut optimization time, delivering 3.3x speedups over SOTA on four datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jiashen Cao (Georgia Institute of Technology)
- 2. Karan Sarkar (Georgia Institute of Technology)
- 3. Ramyad Hadidi (Georgia Institute of Technology)
- 4. Joy Arulraj (Georgia Institute of Technology)
- 5. Hyesoon Kim (Georgia Institute of Technology)
BibTeX Citation
@inproceedings{cao_sigmod22,
title = {{FiGO: Fine-Grained Query Optimization in Video Analytics}},
author = {Cao, Jiashen and Sarkar, Karan and Hadidi, Ramyad and Arulraj, Joy and Kim, Hyesoon},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3517857},
url = {https://dl.acm.org/doi/10.1145/3514221.3517857},
year = {2022}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 100 | LEO - DB2's LEarning Optimizer | 2001 | VLDB | 0.00034385207 |
| 154 | Neo: A Learned Query Optimizer | 2019 | VLDB | 0.00028726181 |
| 295 | Accelerating Machine Learning Inference with Probabilistic Predicates | 2018 | SIGMOD | 0.00022238183 |
| 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 |
| 2,384 | DeepLens: Towards a Visual Data Management System | 2019 | CIDR | 8.6494525e-05 |
| 2,597 | The MemSQL Query Optimizer: A modern optimizer for real-time analytics in a distributed database | 2016 | VLDB | 8.3604418e-05 |
| 2,702 | Panorama: A Data System for Unbounded Vocabulary Querying over Video | 2020 | VLDB | 8.2342712e-05 |
| 3,410 | SVQ: Streaming Video Queries | 2019 | SIGMOD | 7.4362585e-05 |
| 3,720 | VSS: A Storage System for Video Analytics | 2021 | SIGMOD | 7.1739329e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 3,410 | SVQ: Streaming Video Queries | 2019 | SIGMOD |
| 2 | 7,739 | Video-zilla: An Indexing Layer for Large-Scale Video Analytics | 2022 | SIGMOD |
| 3 | 4,007 | Optimizing Video Analytics with Declarative Model Relationships | 2023 | VLDB |
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| 7 | 4,298 | OTIF: Efficient Tracker Pre-processing over Large Video Datasets | 2022 | SIGMOD |
| 8 | 4,429 | Evaluating Temporal Queries Over Video Feeds | 2021 | SIGMOD |
| 9 | 8,589 | Optimizing Video Selection LIMIT Queries With Commonsense Knowledge | 2024 | VLDB |
| 10 | 11,268 | Optimizing Video Queries with Declarative Clues | 2024 | VLDB |