Back to papers
Aero: Adaptive Query Processing of ML Queries
Summary: Aero uses adaptive query processing for ML queries, coping with opaque UDF statistics and data-dependent plan choices. Dynamic predicate evaluation order, runtime UDF routing, and resource reallocation yield up to 6.4x speedups with no accuracy loss.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 7297
- Venue
- SIGMOD
- Year
- 2025
- Pagerank
- 4.7538944e-05
- Overall Rank
- 7,334 | 49.03%
- DOI
-
10.1145/3725408
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 24 of 24 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 116 |
Eddies: Continuously Adaptive Query Processing |
2000 |
SIGMOD |
0.00046191288 |
| 221 |
Efficient Mid-Query Re-Optimization of Sub-Optimal Query Execution Plans |
1998 |
SIGMOD |
0.00033182072 |
| 694 |
BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics |
2020 |
VLDB |
0.00018031141 |
| 1,268 |
Proactive Re-Optimization |
2005 |
SIGMOD |
0.00012914584 |
| 1,390 |
MIRIS: Fast Object Track Queries in Video |
2020 |
SIGMOD |
0.00012242018 |
| 2,089 |
Practical Predicate Placement |
1994 |
SIGMOD |
9.5689208e-05 |
| 3,212 |
Panorama: A Data System for Unbounded Vocabulary Querying over Video |
2020 |
VLDB |
7.3772955e-05 |
| 3,553 |
Approximate Selection with Guarantees using Proxies |
2020 |
VLDB |
6.9763548e-05 |
| 3,599 |
Spatial and Temporal Constrained Ranked Retrieval over Videos |
2022 |
VLDB |
6.92963e-05 |
| 3,610 |
EVA: A Symbolic Approach to Accelerating Exploratory Video Analytics with Materialized Views |
2022 |
SIGMOD |
6.919859e-05 |
| 3,878 |
Data Canopy: Accelerating Exploratory Statistical Analysis |
2017 |
SIGMOD |
6.6669911e-05 |
| 4,492 |
TASTI: Semantic Indexes for Machine Learning-based Queries over Unstructured Data |
2022 |
SIGMOD |
6.1374891e-05 |
| 4,565 |
Optimizing Video Analytics with Declarative Model Relationships |
2023 |
VLDB |
6.0746821e-05 |
| 4,686 |
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures |
2023 |
VLDB |
5.9929067e-05 |
| 4,703 |
Accelerating Approximate Aggregation Queries with Expensive Predicates |
2021 |
VLDB |
5.9793615e-05 |
| 4,890 |
Content-Based Routing: Different Plans for Different Data |
2005 |
VLDB |
5.8477169e-05 |
| 4,947 |
Evaluating Temporal Queries Over Video Feeds |
2021 |
SIGMOD |
5.8107138e-05 |
| 5,062 |
Optimizing Machine Learning Inference Queries with Correlative Proxy Models |
2022 |
VLDB |
5.7172262e-05 |
| 5,168 |
FiGO: Fine-Grained Query Optimization in Video Analytics |
2022 |
SIGMOD |
5.6446115e-05 |
| 5,232 |
Zeus: Efficiently Localizing Actions in Videos using Reinforcement Learning |
2022 |
SIGMOD |
5.6094155e-05 |
| 6,184 |
Top-K Deep Video Analytics: A Probabilistic Approach |
2021 |
SIGMOD |
5.1636368e-05 |
| 6,306 |
Seiden: Revisiting Query Processing in Video Database Systems |
2023 |
VLDB |
5.1146055e-05 |
| 6,868 |
Extract-Transform-Load for Video Streams |
2023 |
VLDB |
4.8982283e-05 |
| 8,382 |
EQUI-VOCAL: Synthesizing Queries for Compositional Video Events from Limited User Interactions |
2023 |
VLDB |
4.5263687e-05 |
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 9,311 |
On Efficient Approximate Queries over Machine Learning Models |
2023 |
VLDB |
4.3535588e-05 |
| 9,788 |
Demonstration of Accelerating Machine Learning Inference Queries with Correlative Proxy Models |
2022 |
VLDB |
4.2799988e-05 |
| 3,345 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
7.1908499e-05 |
| 2,809 |
Extending Relational Query Processing with ML Inference |
2020 |
CIDR |
8.0869552e-05 |
| 5,524 |
Facilitating SQL Query Composition and Analysis |
2020 |
SIGMOD |
5.4589341e-05 |
| 3,409 |
End-to-end Optimization of Machine Learning Prediction Queries |
2022 |
SIGMOD |
7.1240791e-05 |
| 10,641 |
AQETuner: Reliable Query-level Configuration Tuning for Analytical Query Engines |
2025 |
VLDB |
4.1905499e-05 |
| 11,655 |
Query-Driven Learning for Next Generation Predictive Modeling & Analytics |
2019 |
SIGMOD |
4.1905499e-05 |
| 332 |
Accelerating Machine Learning Inference with Probabilistic Predicates |
2018 |
SIGMOD |
0.00027173479 |
| 5,799 |
Learned Approximate Query Processing: Make it Light, Accurate and Fast |
2021 |
CIDR |
5.3219666e-05 |