BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics
Summary: BlazeIt brings declarative FrameQL and video-specific optimization to NN-based spatiotemporal analytics. It uses NNs as control variates for bounded-error approximate aggregates and introduces cardinality-limited search, achieving up to 83× speedups. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Daniel Kang (Stanford University)
- 2. Peter Bailis (Stanford University)
- 3. Matei Zaharia (Stanford University)
BibTeX Citation
@article{kang_vldb20,
title = {{BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics}},
author = {Kang, Daniel and Bailis, Peter and Zaharia, Matei},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {4},
pages = {533--546},
doi = {10.14778/3372716.3372725},
url = {https://doi.org/10.14778/3372716.3372725},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 53 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,472 | PAINE Demo: Optimizing Video Selection Queries With Commonsense Knowledge | 2023 | VLDB | 5.093636e-05 |
| 11,624 | Accelerating Queries over Unstructured Data with ML | 2021 | CIDR | 5.093636e-05 |
| 11,680 | Pool of Experts: Realtime Querying Specialized Knowledge in Massive Neural Networks | 2021 | SIGMOD | 5.093636e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 9,924 | TVM: A Tile-based Video Management Framework | 2024 | VLDB |
| 2 | 4,375 | FiGO: Fine-Grained Query Optimization in Video Analytics | 2022 | SIGMOD |
| 3 | 7,648 | Accelerating Aggregation Queries on Unstructured Streams of Data | 2023 | VLDB |
| 4 | 7,739 | Video-zilla: An Indexing Layer for Large-Scale Video Analytics | 2022 | SIGMOD |
| 5 | 284 | NoScope: Optimizing Neural Network Queries over Video at Scale | 2017 | VLDB |
| 6 | 3,410 | SVQ: Streaming Video Queries | 2019 | SIGMOD |
| 7 | 8,589 | Optimizing Video Selection LIMIT Queries With Commonsense Knowledge | 2024 | VLDB |
| 8 | 4,007 | Optimizing Video Analytics with Declarative Model Relationships | 2023 | VLDB |
| 9 | 11,268 | Optimizing Video Queries with Declarative Clues | 2024 | VLDB |
| 10 | 1,607 | Challenges and Opportunities in DNN-Based Video Analytics: A Demonstration of the BlazeIt Video Query Engine | 2019 | CIDR |