| 333 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027206884 |
| 640 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018759152 |
| 884 |
Plan-Structured Deep Neural Network Models for Query Performance Prediction |
2019 |
VLDB |
0.00015654004 |
| 1,460 |
Benchmarking Learned Indexes |
2021 |
VLDB |
0.00011887068 |
| 1,463 |
ARDA: Automatic Relational Data Augmentation for Machine Learning |
2020 |
VLDB |
0.00011869295 |
| 1,855 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations |
2019 |
SIGMOD |
0.00010315245 |
| 2,783 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
8.1293383e-05 |
| 3,216 |
WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases |
2016 |
VLDB |
7.3601267e-05 |
| 3,658 |
Towards a Hands-Free Query Optimizer through Deep Learning |
2019 |
CIDR |
6.8704209e-05 |
| 4,060 |
CDFShop: Exploring and Optimizing Learned Index Structures |
2020 |
SIGMOD |
6.4836825e-05 |
| 4,417 |
Robust Query Driven Cardinality Estimation under Changing Workloads |
2023 |
VLDB |
6.2037371e-05 |
| 4,593 |
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift |
2023 |
SIGMOD |
6.0606891e-05 |
| 4,961 |
Releasing Cloud Databases from the Chains of Performance Prediction Models |
2017 |
CIDR |
5.7984657e-05 |
| 5,423 |
Kepler: Robust Learning for Faster Parametric Query Optimization |
2023 |
SIGMOD |
5.5130233e-05 |
| 5,451 |
NashDB: An End-to-End Economic Method for Elastic Database Fragmentation, Replication, and Provisioning |
2018 |
SIGMOD |
5.5002949e-05 |
| 5,640 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
5.3933314e-05 |
| 5,832 |
Stage: Query Execution Time Prediction in Amazon Redshift |
2024 |
SIGMOD |
5.3111109e-05 |
| 6,040 |
Steering Query Optimizers: A Practical Take on Big Data Workloads |
2021 |
SIGMOD |
5.2412035e-05 |
| 8,442 |
SageDB: An Instance-Optimized Data Analytics System |
2022 |
VLDB |
4.5120602e-05 |
| 8,659 |
Learned Offline Query Planning via Bayesian Optimization |
2025 |
SIGMOD |
4.4722928e-05 |
| 9,587 |
Low Rank Learning for Offline Query Optimization |
2025 |
SIGMOD |
4.3215645e-05 |
| 9,710 |
QO-Insight: Inspecting Steered Query Optimizers |
2023 |
VLDB |
4.299267e-05 |
| 9,812 |
A Practical Theory of Generalization in Selectivity Learning |
2025 |
VLDB |
4.2783272e-05 |
| 9,981 |
Survivorship Bias in Industrial Database Workloads |
2026 |
CIDR |
4.1945683e-05 |
| 10,045 |
Adaptive Sharding in Untrusted Environments |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,112 |
SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,295 |
Global Hash Tables Strike Back! An Analysis of Parallel GROUP BY Aggregation |
2026 |
VLDB |
4.1945683e-05 |
| 10,452 |
ScaleLLM: A Technique for Scalable LLM-augmented Data Systems |
2025 |
SIGMOD |
4.1945683e-05 |
| 10,619 |
Data-Agnostic Cardinality Learning from Imperfect Workloads |
2025 |
VLDB |
4.1945683e-05 |
| 10,999 |
Towards Full Stack Adaptivity in Permissioned Blockchains |
2024 |
VLDB |
4.1945683e-05 |
| 11,236 |
AdaChain: A Learned Adaptive Blockchain |
2023 |
VLDB |
4.1945683e-05 |
| 11,677 |
NashDB: Fragmentation, Replication, and Provisioning using Economic Methods |
2019 |
VLDB |
4.1945683e-05 |
| 13,160 |
BFTGym: An Interactive Playground for BFT Protocols |
2024 |
VLDB |
- |