| 3,658 |
Towards a Hands-Free Query Optimizer through Deep Learning |
2019 |
CIDR |
6.8704209e-05 |
| 3,702 |
Every Row Counts: Combining Sketches and Sampling for Accurate Group-By Result Estimates |
2019 |
CIDR |
6.8295759e-05 |
| 3,721 |
To Partition, or Not to Partition, That is the Join Question in a Real System |
2021 |
SIGMOD |
6.8179379e-05 |
| 3,725 |
Estimating Cardinalities with Deep Sketches |
2019 |
SIGMOD |
6.8170734e-05 |
| 3,727 |
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection |
2022 |
VLDB |
6.8141709e-05 |
| 3,778 |
A Learned Sketch for Subgraph Counting |
2021 |
SIGMOD |
6.7747398e-05 |
| 3,812 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
6.7373184e-05 |
| 3,922 |
Pushing Data-Induced Predicates Through Joins in Big-Data Clusters |
2020 |
VLDB |
6.6291079e-05 |
| 3,924 |
A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation |
2021 |
SIGMOD |
6.6271553e-05 |
| 3,990 |
FactorJoin: A New Cardinality Estimation Framework for Join Queries |
2023 |
SIGMOD |
6.5581983e-05 |
| 4,012 |
Columnar Storage and List-based Processing for Graph Database Management Systems |
2021 |
VLDB |
6.5335884e-05 |
| 4,127 |
A Statistical Perspective on Discovering Functional Dependencies in Noisy Data |
2020 |
SIGMOD |
6.4310458e-05 |
| 4,158 |
Performance-Optimal Filtering: Bloom Overtakes Cuckoo at High Throughput |
2019 |
VLDB |
6.3994318e-05 |
| 4,174 |
Computation Reuse in Analytics Job Service at Microsoft |
2018 |
SIGMOD |
6.3856219e-05 |
| 4,276 |
Looking Ahead Makes Query Plans Robust: Making the Initial Case with In-Memory Star Schema Data Warehouse Workloads |
2017 |
VLDB |
6.2976602e-05 |
| 4,284 |
HTAP Databases: What is New and What is Next |
2022 |
SIGMOD |
6.2914924e-05 |
| 4,359 |
Astrid: Accurate Selectivity Estimation for String Predicates using Deep Learning |
2021 |
VLDB |
6.2569955e-05 |
| 4,375 |
Sample Debiasing in the Themis Open World Database System |
2020 |
SIGMOD |
6.2427076e-05 |
| 4,417 |
Robust Query Driven Cardinality Estimation under Changing Workloads |
2023 |
VLDB |
6.2037371e-05 |
| 4,434 |
Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process |
2022 |
SIGMOD |
6.1929999e-05 |
| 4,462 |
LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans |
2023 |
VLDB |
6.1611784e-05 |
| 4,523 |
Simplicity Done Right for Join Ordering |
2021 |
CIDR |
6.1135504e-05 |
| 4,543 |
FACE: A Normalizing Flow based Cardinality Estimator |
2022 |
VLDB |
6.1011198e-05 |
| 4,571 |
Adaptive Statistics in Oracle 12c |
2017 |
VLDB |
6.0773174e-05 |
| 4,590 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.0620053e-05 |
| 4,593 |
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift |
2023 |
SIGMOD |
6.0606891e-05 |
| 4,623 |
Automated Generation of Materialized Views in Oracle |
2020 |
VLDB |
6.0411909e-05 |
| 4,661 |
PreQR: Pre-training Representation for SQL Understanding |
2022 |
SIGMOD |
6.0137947e-05 |
| 4,690 |
Deploying a Steered Query Optimizer in Production at Microsoft |
2022 |
SIGMOD |
5.997226e-05 |
| 4,704 |
JSON Tiles: Fast Analytics on Semi-Structured Data |
2021 |
SIGMOD |
5.9853687e-05 |
| 4,842 |
Towards Dynamic and Safe Configuration Tuning for Cloud Databases |
2022 |
SIGMOD |
5.8826802e-05 |
| 4,913 |
UDO: Universal Database Optimization using Reinforcement Learning |
2021 |
VLDB |
5.8316231e-05 |
| 4,999 |
Adaptive Work Placement for Query Processing on Heterogeneous Computing Resources |
2017 |
VLDB |
5.7752801e-05 |
| 5,023 |
GenRewrite: Query Rewriting via Large Language Models |
2026 |
SIGMOD |
5.75363e-05 |
| 5,077 |
HyperBench: A Benchmark and Tool for Hypergraphs and Empirical Findings |
2019 |
PODS |
5.7153846e-05 |
| 5,104 |
Guaranteeing the O~(AGM/OUT) Runtime for Uniform Sampling and Size Estimation over Joins |
2023 |
PODS |
5.6946113e-05 |
| 5,113 |
Columnstore and B+ tree – Are Hybrid Physical Designs Important? |
2018 |
SIGMOD |
5.687445e-05 |
| 5,125 |
The Art of Balance: A RateupDBTM Experience of Building a CPU/GPU Hybrid Database Product |
2021 |
VLDB |
5.679423e-05 |
| 5,315 |
Cuckoo Index: A Lightweight Secondary Index Structure |
2020 |
VLDB |
5.5723424e-05 |
| 5,334 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
5.5649836e-05 |
| 5,337 |
Learned Index Benefits: Machine Learning Based Index Performance Estimation |
2022 |
VLDB |
5.5635208e-05 |
| 5,423 |
Kepler: Robust Learning for Faster Parametric Query Optimization |
2023 |
SIGMOD |
5.5130233e-05 |
| 5,473 |
Facilitating SQL Query Composition and Analysis |
2020 |
SIGMOD |
5.4885366e-05 |
| 5,509 |
Can Large Language Models Predict Data Correlations from Column Names? |
2023 |
VLDB |
5.4703368e-05 |
| 5,530 |
Permutable Compiled Queries: Dynamically Adapting Compiled Queries without Recompiling |
2021 |
VLDB |
5.4554282e-05 |
| 5,535 |
Lightweight Cardinality Estimation in LSM-based Systems |
2018 |
SIGMOD |
5.4539235e-05 |
| 5,633 |
Analyzing the Impact of Cardinality Estimation on Execution Plans in Microsoft SQL Server |
2023 |
VLDB |
5.4011156e-05 |
| 5,637 |
Database Workload Characterization with Query Plan Encoders |
2022 |
VLDB |
5.3979505e-05 |
| 5,640 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
5.3933314e-05 |
| 5,645 |
Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts |
2022 |
SIGMOD |
5.3923454e-05 |