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)
Incoming Non-self Citations Over Time
Authors
- 1. Gaurav Tarlok Kakkar (Georgia Institute of Technology)
- 2. Jiashen Cao (Georgia Institute of Technology)
- 3. Aubhro Sengupta (Georgia Institute of Technology)
- 4. Joy Arulraj (Georgia Institute of Technology)
- 5. Hyesoon Kim (Georgia Institute of Technology)
BibTeX Citation
@inproceedings{kakkar_sigmod25,
title = {{Aero: Adaptive Query Processing of ML Queries}},
author = {Kakkar, Gaurav Tarlok and Cao, Jiashen and Sengupta, Aubhro and Arulraj, Joy and Kim, Hyesoon},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725408},
url = {https://dl.acm.org/doi/10.1145/3725408},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 9 of 9 citing papers.
Previous
Page 1 / 1
Next
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.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 11,052 | A Portable Middleware for Plan-Based Adaptive Query Processing | 2026 | VLDB |
| 2 | 2,190 | Lero: A Learning-to-Rank Query Optimizer | 2023 | VLDB |
| 3 | 2,199 | Extending Relational Query Processing with ML Inference | 2020 | CIDR |
| 4 | 10,479 | A Backend-Agnostic Compiler for Approximate Query Processing with Probabilistic Tensor Algebra | 2026 | SIGMOD |
| 5 | 5,160 | Facilitating SQL Query Composition and Analysis | 2020 | SIGMOD |
| 6 | 2,672 | End-to-end Optimization of Machine Learning Prediction Queries | 2022 | SIGMOD |
| 7 | 11,350 | AQETuner: Reliable Query-level Configuration Tuning for Analytical Query Engines | 2025 | VLDB |
| 8 | 12,194 | Query-Driven Learning for Next Generation Predictive Modeling & Analytics | 2019 | SIGMOD |
| 9 | 278 | Accelerating Machine Learning Inference with Probabilistic Predicates | 2018 | SIGMOD |
| 10 | 4,792 | Learned Approximate Query Processing: Make it Light, Accurate and Fast | 2021 | CIDR |