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 7 of 57 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,210 | Scalable Complex Event Processing on Video Streams | 2025 | SIGMOD | 4.9793485e-05 |
| 11,310 | Deja Vu: Efficient Video-Language Query Engine with Learning-based Inter-Frame Computation Reuse | 2025 | VLDB | 4.9793485e-05 |
| 11,509 | Predictive and Near-Optimal Sampling for View Materialization in Video Databases | 2024 | SIGMOD | 4.9793485e-05 |
| 11,596 | Optimizing Video Queries with Declarative Clues | 2024 | VLDB | 4.9793485e-05 |
| 11,783 | PAINE Demo: Optimizing Video Selection Queries With Commonsense Knowledge | 2023 | VLDB | 4.9793485e-05 |
| 11,931 | Accelerating Queries over Unstructured Data with ML | 2021 | CIDR | 4.9793485e-05 |
| 11,987 | Pool of Experts: Realtime Querying Specialized Knowledge in Massive Neural Networks | 2021 | SIGMOD | 4.9793485e-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.
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