| 84 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.00035838391 |
| 154 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00028726181 |
| 323 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00021264788 |
| 378 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00019638121 |
| 401 |
Deep Unsupervised Cardinality Estimation |
2020 |
VLDB |
0.00019092557 |
| 422 |
Umbra: A Disk-Based System with In-Memory Performance |
2020 |
CIDR |
0.00018732744 |
| 465 |
An End-to-End Learning-based Cost Estimator |
2020 |
VLDB |
0.0001803934 |
| 513 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00017190574 |
| 694 |
Cardinality Estimation Done Right: Index-Based Join Sampling |
2017 |
CIDR |
0.00014911698 |
| 772 |
VerdictDB: Universalizing Approximate Query Processing |
2018 |
SIGMOD |
0.00014147905 |
| 809 |
Optimizing Subgraph Queries by Combining Binary and Worst-Case Optimal Joins |
2019 |
VLDB |
0.00013874588 |
| 1,061 |
Are We Ready For Learned Cardinality Estimation? |
2021 |
VLDB |
0.00012369764 |
| 1,122 |
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation |
2022 |
VLDB |
0.0001209124 |
| 1,241 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
0.00011521639 |
| 1,279 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations |
2019 |
SIGMOD |
0.00011361878 |
| 1,286 |
Adaptive Optimization of Very Large Join Queries |
2018 |
SIGMOD |
0.00011320736 |
| 1,468 |
Towards a Learning Optimizer for Shared Clouds |
2019 |
VLDB |
0.00010686496 |
| 1,481 |
Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms |
2020 |
VLDB |
0.00010644613 |
| 1,499 |
Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities |
2019 |
SIGMOD |
0.00010564536 |
| 1,500 |
Efficient Discovery of Approximate Dependencies |
2018 |
VLDB |
0.00010561098 |
| 1,536 |
Improved Selectivity Estimation by Combining Knowledge from Sampling and Synopses |
2018 |
VLDB |
0.00010460864 |
| 1,548 |
Automatically Indexing Millions of Databases in Microsoft Azure SQL Database |
2019 |
SIGMOD |
0.00010392475 |
| 1,573 |
Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries |
2020 |
SIGMOD |
0.00010328171 |
| 1,712 |
SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning |
2019 |
SIGMOD |
9.9492299e-05 |
| 1,740 |
Adopting Worst-Case Optimal Joins in Relational Database Systems |
2020 |
VLDB |
9.875587e-05 |
| 1,815 |
SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning |
2018 |
VLDB |
9.6894541e-05 |
| 1,832 |
DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems |
2021 |
VLDB |
9.6607418e-05 |
| 1,876 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
9.5717543e-05 |
| 1,892 |
Procedural Extensions of SQL: Understanding their usage in the wild |
2021 |
VLDB |
9.5277793e-05 |
| 1,949 |
Diagnosing Root Causes of Intermittent Slow Queries in Cloud Databases |
2020 |
VLDB |
9.430385e-05 |
| 1,988 |
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation |
2021 |
VLDB |
9.3501502e-05 |
| 2,202 |
Quantifying TPC-H Choke Points and Their Optimizations |
2020 |
VLDB |
8.9639459e-05 |
| 2,203 |
Estimating Join Selectivities using Bandwidth-Optimized Kernel Density Models |
2017 |
VLDB |
8.9610447e-05 |
| 2,355 |
QueryFormer: A Tree Transformer Model for Query Plan Representation |
2022 |
VLDB |
8.7022189e-05 |
| 2,420 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
8.605257e-05 |
| 2,424 |
Computing the Shapley Value of Facts in Query Answering |
2022 |
SIGMOD |
8.6009068e-05 |
| 2,452 |
A Learned Query Rewrite System using Monte Carlo Tree Search |
2022 |
VLDB |
8.5584e-05 |
| 2,499 |
Learning a Partitioning Advisor for Cloud Databases |
2020 |
SIGMOD |
8.4993549e-05 |
| 2,521 |
CodexDB: Synthesizing Code for Query Processing from Natural Language Instructions using GPT-3 Codex |
2022 |
VLDB |
8.4729505e-05 |
| 2,543 |
Learned Cardinality Estimation: An In-depth Study |
2022 |
SIGMOD |
8.4445934e-05 |
| 2,553 |
LLM-R^2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency |
2025 |
VLDB |
8.4283807e-05 |
| 2,620 |
Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation |
2021 |
VLDB |
8.3363963e-05 |
| 2,723 |
Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation |
2022 |
VLDB |
8.2049453e-05 |
| 2,731 |
Neural Subgraph Counting with Wasserstein Estimator |
2022 |
SIGMOD |
8.1959181e-05 |
| 2,740 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
8.1855759e-05 |
| 2,762 |
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection |
2022 |
VLDB |
8.1539867e-05 |
| 2,788 |
BtrBlocks: Efficient Columnar Compression for Data Lakes |
2023 |
SIGMOD |
8.1205155e-05 |
| 2,812 |
Query Performance Prediction for Concurrent Queries using Graph Embedding |
2020 |
VLDB |
8.0979597e-05 |
| 2,822 |
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings |
2020 |
SIGMOD |
8.0898536e-05 |
| 2,940 |
G-CARE: A Framework for Performance Benchmarking of Cardinality Estimation Techniques for Subgraph Matching |
2020 |
SIGMOD |
7.9381573e-05 |