| 4,132 |
SQLStorm: Taking Database Benchmarking into the LLM Era |
2025 |
VLDB |
6.7885553e-05 |
| 4,137 |
The Art of Balance: A RateupDB Experience of Building a CPU/GPU Hybrid Database Product |
2021 |
VLDB |
6.7861661e-05 |
| 4,202 |
Kepler: Robust Learning for Faster Parametric Query Optimization |
2023 |
SIGMOD |
6.7374091e-05 |
| 4,293 |
A Method for Optimizing Opaque Filter Queries |
2020 |
SIGMOD |
6.6819917e-05 |
| 4,311 |
ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads |
2024 |
VLDB |
6.6727978e-05 |
| 4,457 |
Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process |
2022 |
SIGMOD |
6.5913732e-05 |
| 4,538 |
One Model to Rule them All: Towards Zero-Shot Learning for Databases |
2022 |
CIDR |
6.553705e-05 |
| 4,563 |
Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts |
2022 |
SIGMOD |
6.5320994e-05 |
| 4,683 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
6.4716143e-05 |
| 4,688 |
Learned Cardinality Estimation for Similarity Queries |
2021 |
SIGMOD |
6.4697463e-05 |
| 4,707 |
PreQR: Pre-training Representation for SQL Understanding |
2022 |
SIGMOD |
6.4587914e-05 |
| 4,711 |
Learned Index Benefits: Machine Learning Based Index Performance Estimation |
2022 |
VLDB |
6.4573842e-05 |
| 4,741 |
Machine Learning for Databases |
2021 |
VLDB |
6.4410027e-05 |
| 4,781 |
Learned Approximate Query Processing: Make it Light, Accurate and Fast |
2021 |
CIDR |
6.4162085e-05 |
| 4,852 |
LpBound: Pessimistic Cardinality Estimation using ℓp-Norms of Degree Sequences |
2025 |
SIGMOD |
6.3806134e-05 |
| 4,890 |
Can Learned Models Replace Hash Functions? |
2023 |
VLDB |
6.3682031e-05 |
| 4,950 |
Debunking the Myth of Join Ordering: Toward Robust SQL Analytics |
2025 |
SIGMOD |
6.3421691e-05 |
| 5,003 |
COMPASS: Online Sketch-based Query Optimization for In-Memory Databases |
2021 |
SIGMOD |
6.3188773e-05 |
| 5,022 |
Analyzing the Impact of Cardinality Estimation on Execution Plans in Microsoft SQL Server |
2023 |
VLDB |
6.3100988e-05 |
| 5,041 |
LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems |
2022 |
SIGMOD |
6.3006152e-05 |
| 5,110 |
Steering Query Optimizers: A Practical Take on Big Data Workloads |
2021 |
SIGMOD |
6.269351e-05 |
| 5,126 |
Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach |
2020 |
SIGMOD |
6.261175e-05 |
| 5,209 |
SAM: Database Generation from Query Workloads with Supervised Autoregressive Models |
2022 |
SIGMOD |
6.2262056e-05 |
| 5,219 |
SafeBound: A Practical System for Generating Cardinality Bounds |
2023 |
SIGMOD |
6.222726e-05 |
| 5,241 |
FASTgres: Making Learned Query Optimizer Hinting Effective |
2023 |
VLDB |
6.2154384e-05 |
| 5,456 |
Eraser: Eliminating Performance Regression on Learned Query Optimizer |
2024 |
VLDB |
6.1239873e-05 |
| 5,481 |
A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies |
2024 |
VLDB |
6.1125124e-05 |
| 5,649 |
Sample-Efficient Cardinality Estimation Using Geometric Deep Learning |
2024 |
VLDB |
6.052326e-05 |
| 5,674 |
HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning |
2023 |
VLDB |
6.0430252e-05 |
| 5,683 |
How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks |
2025 |
SIGMOD |
6.0392183e-05 |
| 5,716 |
Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data |
2023 |
SIGMOD |
6.0194657e-05 |
| 5,831 |
Joins on Samples: A Theoretical Guide for Practitioners |
2020 |
VLDB |
5.9782109e-05 |
| 5,840 |
Pre-training Summarization Models of Structured Datasets for Cardinality Estimation |
2022 |
VLDB |
5.9737703e-05 |
| 5,865 |
Modeling Shifting Workloads for Learned Database Systems |
2024 |
SIGMOD |
5.9659203e-05 |
| 5,871 |
PilotScope: Steering Databases with Machine Learning Drivers |
2024 |
VLDB |
5.9639223e-05 |
| 5,908 |
Quantum-Inspired Digital Annealing for Join Ordering |
2024 |
VLDB |
5.9506986e-05 |
| 5,974 |
Towards instance-optimized data systems |
2021 |
VLDB |
5.9305575e-05 |
| 6,080 |
From Auto-tuning One Size Fits All to Self-designed and Learned Data-intensive Systems |
2019 |
SIGMOD |
5.8924903e-05 |
| 6,105 |
Breaking It Down: An In-depth Study of Index Advisors |
2024 |
VLDB |
5.8860941e-05 |
| 6,141 |
Expand your Training Limits! Generating Training Data for ML-based Data Management |
2021 |
SIGMOD |
5.8733296e-05 |
| 6,416 |
A Unified Transferable Model for ML-Enhanced DBMS |
2022 |
CIDR |
5.7920805e-05 |
| 6,469 |
Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks |
2024 |
SIGMOD |
5.7743636e-05 |
| 6,660 |
Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation |
2023 |
SIGMOD |
5.7178404e-05 |
| 6,710 |
Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis |
2023 |
VLDB |
5.7019157e-05 |
| 6,753 |
Multi-Tenant Cloud Data Services: State-of-the-Art, Challenges and Opportunities |
2022 |
SIGMOD |
5.6904085e-05 |
| 6,791 |
dbET: Execution Time Distribution-based Plan Selection |
2023 |
SIGMOD |
5.6811782e-05 |
| 6,818 |
ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation |
2024 |
SIGMOD |
5.672718e-05 |
| 6,884 |
LPLM: A Neural Language Model for Cardinality Estimation of LIKE-Queries |
2024 |
SIGMOD |
5.6563432e-05 |
| 7,033 |
Rethinking Learned Cost Models: Why Start from Scratch? |
2023 |
SIGMOD |
5.6168499e-05 |
| 7,166 |
Learning to Sample: Counting with Complex Queries |
2020 |
VLDB |
5.5949741e-05 |