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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
- 7296
- Venue
- SIGMOD
- Year
- 2025
- Pagerank
- 4.7584583e-05
- Overall Rank
- 7,338 | 48.96%
- 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 |
| 115 |
Eddies: Continuously Adaptive Query Processing |
2000 |
SIGMOD |
0.00046221215 |
| 220 |
Efficient Mid-Query Re-Optimization of Sub-Optimal Query Execution Plans |
1998 |
SIGMOD |
0.00033194808 |
| 696 |
BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics |
2020 |
VLDB |
0.00018048935 |
| 1,272 |
Proactive Re-Optimization |
2005 |
SIGMOD |
0.00012920076 |
| 1,388 |
MIRIS: Fast Object Track Queries in Video |
2020 |
SIGMOD |
0.00012260926 |
| 2,086 |
Practical Predicate Placement |
1994 |
SIGMOD |
9.5779956e-05 |
| 3,206 |
Panorama: A Data System for Unbounded Vocabulary Querying over Video |
2020 |
VLDB |
7.3826363e-05 |
| 3,558 |
Approximate Selection with Guarantees using Proxies |
2020 |
VLDB |
6.9765724e-05 |
| 3,604 |
Spatial and Temporal Constrained Ranked Retrieval over Videos |
2022 |
VLDB |
6.9301368e-05 |
| 3,606 |
EVA: A Symbolic Approach to Accelerating Exploratory Video Analytics with Materialized Views |
2022 |
SIGMOD |
6.9260354e-05 |
| 3,878 |
Data Canopy: Accelerating Exploratory Statistical Analysis |
2017 |
SIGMOD |
6.6731435e-05 |
| 4,501 |
TASTI: Semantic Indexes for Machine Learning-based Queries over Unstructured Data |
2022 |
SIGMOD |
6.137686e-05 |
| 4,567 |
Optimizing Video Analytics with Declarative Model Relationships |
2023 |
VLDB |
6.080526e-05 |
| 4,687 |
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures |
2023 |
VLDB |
5.9986055e-05 |
| 4,712 |
Accelerating Approximate Aggregation Queries with Expensive Predicates |
2021 |
VLDB |
5.9787986e-05 |
| 4,883 |
Content-Based Routing: Different Plans for Different Data |
2005 |
VLDB |
5.8545658e-05 |
| 4,950 |
Evaluating Temporal Queries Over Video Feeds |
2021 |
SIGMOD |
5.8104133e-05 |
| 5,072 |
Optimizing Machine Learning Inference Queries with Correlative Proxy Models |
2022 |
VLDB |
5.7185674e-05 |
| 5,135 |
Zeus: Efficiently Localizing Actions in Videos using Reinforcement Learning |
2022 |
SIGMOD |
5.6724721e-05 |
| 5,173 |
FiGO: Fine-Grained Query Optimization in Video Analytics |
2022 |
SIGMOD |
5.6447253e-05 |
| 6,182 |
Top-K Deep Video Analytics: A Probabilistic Approach |
2021 |
SIGMOD |
5.1682689e-05 |
| 6,315 |
Seiden: Revisiting Query Processing in Video Database Systems |
2023 |
VLDB |
5.1142298e-05 |
| 6,877 |
Extract-Transform-Load for Video Streams |
2023 |
VLDB |
4.8974054e-05 |
| 8,383 |
EQUI-VOCAL: Synthesizing Queries for Compositional Video Events from Limited User Interactions |
2023 |
VLDB |
4.5307128e-05 |
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Venue |
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On Efficient Approximate Queries over Machine Learning Models |
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| 9,807 |
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Lero: A Learning-to-Rank Query Optimizer |
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7.1904529e-05 |
| 2,804 |
Extending Relational Query Processing with ML Inference |
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CIDR |
8.0935487e-05 |
| 5,473 |
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| 3,407 |
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SIGMOD |
7.1295646e-05 |
| 10,633 |
AQETuner: Reliable Query-level Configuration Tuning for Analytical Query Engines |
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VLDB |
4.1945683e-05 |
| 11,650 |
Query-Driven Learning for Next Generation Predictive Modeling & Analytics |
2019 |
SIGMOD |
4.1945683e-05 |
| 329 |
Accelerating Machine Learning Inference with Probabilistic Predicates |
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SIGMOD |
0.00027249545 |
| 6,230 |
Learned Approximate Query Processing: Make it Light, Accurate and Fast |
2021 |
CIDR |
5.145989e-05 |