| 145 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.0002908188 |
| 362 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00019989474 |
| 560 |
Plan-Structured Deep Neural Network Models for Query Performance Prediction |
2019 |
VLDB |
0.00016403151 |
| 848 |
Benchmarking Learned Indexes |
2021 |
VLDB |
0.00013506188 |
| 1,038 |
ARDA: Automatic Relational Data Augmentation for Machine Learning |
2020 |
VLDB |
0.00012370691 |
| 1,279 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations |
2019 |
SIGMOD |
0.00011226878 |
| 1,734 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
9.7545773e-05 |
| 2,981 |
WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases |
2016 |
VLDB |
7.7851845e-05 |
| 3,060 |
Towards a Hands-Free Query Optimizer through Deep Learning |
2019 |
CIDR |
7.6928239e-05 |
| 3,206 |
CDFShop: Exploring and Optimizing Learned Index Structures |
2020 |
SIGMOD |
7.5397402e-05 |
| 3,327 |
Robust Query Driven Cardinality Estimation under Changing Workloads |
2023 |
VLDB |
7.4207879e-05 |
| 3,563 |
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift |
2023 |
SIGMOD |
7.2042148e-05 |
| 4,202 |
Kepler: Robust Learning for Faster Parametric Query Optimization |
2023 |
SIGMOD |
6.7374091e-05 |
| 4,683 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
6.4716143e-05 |
| 5,087 |
Releasing Cloud Databases from the Chains of Performance Prediction Models |
2017 |
CIDR |
6.2809904e-05 |
| 5,110 |
Steering Query Optimizers: A Practical Take on Big Data Workloads |
2021 |
SIGMOD |
6.269351e-05 |
| 5,159 |
NashDB: An End-to-End Economic Method for Elastic Database Fragmentation, Replication, and Provisioning |
2018 |
SIGMOD |
6.2482448e-05 |
| 5,214 |
Stage: Query Execution Time Prediction in Amazon Redshift |
2024 |
SIGMOD |
6.2248104e-05 |
| 7,598 |
SageDB: An Instance-Optimized Data Analytics System |
2022 |
VLDB |
5.4871733e-05 |
| 8,332 |
Learned Offline Query Planning via Bayesian Optimization |
2025 |
SIGMOD |
5.3528188e-05 |
| 9,561 |
Global Hash Tables Strike Back! An Analysis of Parallel GROUP BY Aggregation |
2026 |
VLDB |
5.1571823e-05 |
| 9,670 |
Low Rank Learning for Offline Query Optimization |
2025 |
SIGMOD |
5.1452097e-05 |
| 9,718 |
A Practical Theory of Generalization in Selectivity Learning |
2025 |
VLDB |
5.1353964e-05 |
| 10,030 |
QO-Insight: Inspecting Steered Query Optimizers |
2023 |
VLDB |
5.0925155e-05 |
| 10,281 |
Adaptive Sharding in Untrusted Environments |
2026 |
SIGMOD |
5.0448662e-05 |
| 10,285 |
Towards Full Stack Adaptivity in Permissioned Blockchains |
2024 |
VLDB |
5.0448662e-05 |
| 10,287 |
AdaChain: A Learned Adaptive Blockchain |
2023 |
VLDB |
5.0448662e-05 |
| 10,294 |
Data-Agnostic Cardinality Learning from Imperfect Workloads |
2025 |
VLDB |
5.0431863e-05 |
| 10,350 |
Survivorship Bias in Industrial Database Workloads |
2026 |
CIDR |
4.9793485e-05 |
| 10,596 |
SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer |
2026 |
SIGMOD |
4.9793485e-05 |
| 10,941 |
Ultron: History-Based Query Optimization at Databricks |
2026 |
VLDB |
4.9793485e-05 |
| 11,161 |
ScaleLLM: A Technique for Scalable LLM-augmented Data Systems |
2025 |
SIGMOD |
4.9793485e-05 |
| 12,172 |
NashDB: Fragmentation, Replication, and Provisioning using Economic Methods |
2019 |
VLDB |
4.9793485e-05 |
| 13,680 |
BFTGym: An Interactive Playground for BFT Protocols |
2024 |
VLDB |
- |