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AQETuner: Reliable Query-level Configuration Tuning for Analytical Query Engines
Summary: AQETuner: BO-based per-query tuner for analytical query engines using attention to jointly encode knobs + query plans, capturing knob→plan effects. Dual-task Neural Process predicts latency and failures while PSO supplies parallel cold-start samples, improving latency 23.7% and failures 51.2%.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13913
- Venue
- VLDB
- Year
- 2025
- Pagerank
- 4.1945683e-05
- Overall Rank
- 10,633 | 26.03%
- DOI
-
10.14778/3742728.3742759
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No non-self incoming citations found for this paper in this database.
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 22 of 22 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059038975 |
| 167 |
The Snowflake Elastic Data Warehouse |
2016 |
SIGMOD |
0.00039180521 |
| 183 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00036721403 |
| 424 |
Tuning Database Configuration Parameters with iTuned |
2009 |
VLDB |
0.00023616398 |
| 514 |
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning |
2019 |
SIGMOD |
0.0002124895 |
| 629 |
Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors |
2009 |
VLDB |
0.00018942366 |
| 640 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018759152 |
| 782 |
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning |
2019 |
VLDB |
0.00016729063 |
| 1,354 |
Managing Intra-operator Parallelism in Parallel Database Systems |
1995 |
VLDB |
0.00012408166 |
| 1,638 |
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation |
2022 |
VLDB |
0.00011049779 |
| 1,902 |
Black or White? How to Develop an AutoTuner for Memory-based Analytics |
2020 |
SIGMOD |
0.00010157713 |
| 3,169 |
QueryFormer: A Tree Transformer Model for Query Plan Representation |
2022 |
VLDB |
7.4498425e-05 |
| 3,522 |
ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases |
2021 |
SIGMOD |
7.0096727e-05 |
| 3,812 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
6.7373184e-05 |
| 4,265 |
CGPTuner: a Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations Under Varying Workload Conditions |
2021 |
VLDB |
6.3097793e-05 |
| 4,399 |
HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements |
2022 |
SIGMOD |
6.2225151e-05 |
| 4,543 |
FACE: A Normalizing Flow based Cardinality Estimator |
2022 |
VLDB |
6.1011198e-05 |
| 4,842 |
Towards Dynamic and Safe Configuration Tuning for Cloud Databases |
2022 |
SIGMOD |
5.8826802e-05 |
| 5,531 |
Presto: A Decade of SQL Analytics at Meta |
2023 |
SIGMOD |
5.4549499e-05 |
| 7,330 |
Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries |
2023 |
SIGMOD |
4.7609373e-05 |
| 8,046 |
Bouncer: Admission Control with Response Time Objectives for Low-latency Online Data Systems |
2024 |
SIGMOD |
4.598406e-05 |
| 8,834 |
ByteCard: Enhancing ByteDance’s Data Warehouse with Learned Cardinality Estimation |
2024 |
SIGMOD |
4.4394021e-05 |
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