| 142 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
180 |
0.00029114937 |
| 355 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
132 |
0.00020146245 |
| 545 |
Plan-Structured Deep Neural Network Models for Query Performance Prediction |
2019 |
VLDB |
79 |
0.0001657599 |
| 834 |
Benchmarking Learned Indexes |
2021 |
VLDB |
61 |
0.00013539098 |
| 1,041 |
ARDA: Automatic Relational Data Augmentation for Machine Learning |
2020 |
VLDB |
35 |
0.00012334538 |
| 1,282 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations |
2019 |
SIGMOD |
59 |
0.00011197535 |
| 1,740 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
60 |
9.7289523e-05 |
| 2,892 |
WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases |
2016 |
VLDB |
21 |
7.8872299e-05 |
| 3,062 |
Towards a Hands-Free Query Optimizer through Deep Learning |
2019 |
CIDR |
19 |
7.6809918e-05 |
| 3,207 |
CDFShop: Exploring and Optimizing Learned Index Structures |
2020 |
SIGMOD |
21 |
7.5187675e-05 |
| 3,269 |
Robust Query Driven Cardinality Estimation under Changing Workloads |
2023 |
VLDB |
38 |
7.463682e-05 |
| 3,328 |
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift |
2023 |
SIGMOD |
27 |
7.4094448e-05 |
| 4,202 |
Kepler: Robust Learning for Faster Parametric Query Optimization |
2023 |
SIGMOD |
23 |
6.7258607e-05 |
| 4,683 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
22 |
6.4545989e-05 |
| 4,833 |
Stage: Query Execution Time Prediction in Amazon Redshift |
2024 |
SIGMOD |
23 |
6.3834856e-05 |
| 5,097 |
Releasing Cloud Databases from the Chains of Performance Prediction Models |
2017 |
CIDR |
7 |
6.2632543e-05 |
| 5,126 |
Steering Query Optimizers: A Practical Take on Big Data Workloads |
2021 |
SIGMOD |
22 |
6.2493606e-05 |
| 5,181 |
NashDB: An End-to-End Economic Method for Elastic Database Fragmentation, Replication, and Provisioning |
2018 |
SIGMOD |
8 |
6.2255681e-05 |
| 7,561 |
Learned Offline Query Planning via Bayesian Optimization |
2025 |
SIGMOD |
9 |
5.4784915e-05 |
| 7,621 |
SageDB: An Instance-Optimized Data Analytics System |
2022 |
VLDB |
8 |
5.4664683e-05 |
| 9,594 |
Global Hash Tables Strike Back! An Analysis of Parallel GROUP BY Aggregation |
2026 |
VLDB |
1 |
5.1376962e-05 |
| 9,662 |
Low Rank Learning for Offline Query Optimization |
2025 |
SIGMOD |
5 |
5.1283524e-05 |
| 9,753 |
A Practical Theory of Generalization in Selectivity Learning |
2025 |
VLDB |
3 |
5.1159926e-05 |
| 10,074 |
QO-Insight: Inspecting Steered Query Optimizers |
2023 |
VLDB |
2 |
5.0732737e-05 |
| 10,314 |
Adaptive Sharding in Untrusted Environments |
2026 |
SIGMOD |
1 |
5.0258044e-05 |
| 10,318 |
Towards Full Stack Adaptivity in Permissioned Blockchains |
2024 |
VLDB |
3 |
5.0258044e-05 |
| 10,320 |
AdaChain: A Learned Adaptive Blockchain |
2023 |
VLDB |
4 |
5.0258044e-05 |
| 10,327 |
Data-Agnostic Cardinality Learning from Imperfect Workloads |
2025 |
VLDB |
1 |
5.0241309e-05 |
| 10,429 |
Survivorship Bias in Industrial Database Workloads |
2026 |
CIDR |
1 |
4.9605343e-05 |
| 10,668 |
SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer |
2026 |
SIGMOD |
2 |
4.9605343e-05 |
| 11,011 |
Ultron: History-Based Query Optimization at Databricks |
2026 |
VLDB |
0 |
4.9605343e-05 |
| 11,229 |
ScaleLLM: A Technique for Scalable LLM-augmented Data Systems |
2025 |
SIGMOD |
0 |
4.9605343e-05 |
| 12,221 |
NashDB: Fragmentation, Replication, and Provisioning using Economic Methods |
2019 |
VLDB |
0 |
4.9605343e-05 |
| 13,725 |
BFTGym: An Interactive Playground for BFT Protocols |
2024 |
VLDB |
1 |
- |