| 144 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
179 |
0.00029090793 |
| 361 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
130 |
0.00020000855 |
| 560 |
Plan-Structured Deep Neural Network Models for Query Performance Prediction |
2019 |
VLDB |
77 |
0.00016408613 |
| 835 |
Benchmarking Learned Indexes |
2021 |
VLDB |
61 |
0.00013575971 |
| 1,038 |
ARDA: Automatic Relational Data Augmentation for Machine Learning |
2020 |
VLDB |
35 |
0.000123653 |
| 1,280 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations |
2019 |
SIGMOD |
59 |
0.00011224914 |
| 1,735 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
60 |
9.7566604e-05 |
| 2,983 |
WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases |
2016 |
VLDB |
20 |
7.7831414e-05 |
| 3,057 |
Towards a Hands-Free Query Optimizer through Deep Learning |
2019 |
CIDR |
19 |
7.6960881e-05 |
| 3,198 |
CDFShop: Exploring and Optimizing Learned Index Structures |
2020 |
SIGMOD |
21 |
7.5422544e-05 |
| 3,327 |
Robust Query Driven Cardinality Estimation under Changing Workloads |
2023 |
VLDB |
37 |
7.4233639e-05 |
| 3,565 |
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift |
2023 |
SIGMOD |
25 |
7.200937e-05 |
| 4,191 |
Kepler: Robust Learning for Faster Parametric Query Optimization |
2023 |
SIGMOD |
23 |
6.7425275e-05 |
| 4,677 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
22 |
6.4721041e-05 |
| 5,090 |
Releasing Cloud Databases from the Chains of Performance Prediction Models |
2017 |
CIDR |
7 |
6.278054e-05 |
| 5,110 |
Steering Query Optimizers: A Practical Take on Big Data Workloads |
2021 |
SIGMOD |
22 |
6.2678118e-05 |
| 5,160 |
NashDB: An End-to-End Economic Method for Elastic Database Fragmentation, Replication, and Provisioning |
2018 |
SIGMOD |
8 |
6.2454263e-05 |
| 5,216 |
Stage: Query Execution Time Prediction in Amazon Redshift |
2024 |
SIGMOD |
21 |
6.2218868e-05 |
| 7,546 |
Learned Offline Query Planning via Bayesian Optimization |
2025 |
SIGMOD |
9 |
5.4966669e-05 |
| 7,604 |
SageDB: An Instance-Optimized Data Analytics System |
2022 |
VLDB |
8 |
5.4846038e-05 |
| 9,569 |
Global Hash Tables Strike Back! An Analysis of Parallel GROUP BY Aggregation |
2026 |
VLDB |
1 |
5.154741e-05 |
| 9,635 |
Low Rank Learning for Offline Query Optimization |
2025 |
SIGMOD |
5 |
5.1453041e-05 |
| 9,723 |
A Practical Theory of Generalization in Selectivity Learning |
2025 |
VLDB |
3 |
5.1329654e-05 |
| 10,035 |
QO-Insight: Inspecting Steered Query Optimizers |
2023 |
VLDB |
2 |
5.0901047e-05 |
| 10,287 |
Adaptive Sharding in Untrusted Environments |
2026 |
SIGMOD |
1 |
5.042478e-05 |
| 10,291 |
Towards Full Stack Adaptivity in Permissioned Blockchains |
2024 |
VLDB |
3 |
5.042478e-05 |
| 10,293 |
AdaChain: A Learned Adaptive Blockchain |
2023 |
VLDB |
4 |
5.042478e-05 |
| 10,300 |
Data-Agnostic Cardinality Learning from Imperfect Workloads |
2025 |
VLDB |
1 |
5.0407989e-05 |
| 10,362 |
Survivorship Bias in Industrial Database Workloads |
2026 |
CIDR |
1 |
4.9769913e-05 |
| 10,607 |
SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer |
2026 |
SIGMOD |
2 |
4.9769913e-05 |
| 10,950 |
Ultron: History-Based Query Optimization at Databricks |
2026 |
VLDB |
0 |
4.9769913e-05 |
| 11,170 |
ScaleLLM: A Technique for Scalable LLM-augmented Data Systems |
2025 |
SIGMOD |
0 |
4.9769913e-05 |
| 12,178 |
NashDB: Fragmentation, Replication, and Provisioning using Economic Methods |
2019 |
VLDB |
0 |
4.9769913e-05 |
| 13,685 |
BFTGym: An Interactive Playground for BFT Protocols |
2024 |
VLDB |
1 |
- |