| 8,049 |
Thrifty Query Execution via Incrementability |
2020 |
SIGMOD |
4.5939412e-05 |
| 8,149 |
Efficiently Computing Join Orders with Heuristic Search |
2023 |
SIGMOD |
4.5715614e-05 |
| 8,162 |
MONSOON: Multi-Step Optimization and Execution of Queries with Partially Obscured Predicates |
2020 |
SIGMOD |
4.5686903e-05 |
| 8,219 |
PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! |
2021 |
VLDB |
4.551524e-05 |
| 8,339 |
SlabCity: Whole-Query Optimization using Program Synthesis |
2023 |
VLDB |
4.5383933e-05 |
| 8,365 |
NeurDB: On the Design and Implementation of an AI-powered Autonomous Database |
2025 |
CIDR |
4.5305127e-05 |
| 8,378 |
Towards Building Autonomous Data Services on Azure |
2023 |
SIGMOD |
4.5275731e-05 |
| 8,411 |
The Case for Learned In-Memory Joins |
2023 |
VLDB |
4.5151296e-05 |
| 8,434 |
SageDB: An Instance-Optimized Data Analytics System |
2022 |
VLDB |
4.5077955e-05 |
| 8,479 |
Can Large Language Models Be Query Optimizer for Relational Databases? |
2026 |
SIGMOD |
4.4967983e-05 |
| 8,575 |
Robust and Budget-Constrained Encoding Configurations for In-Memory Database Systems |
2022 |
VLDB |
4.4880409e-05 |
| 8,579 |
Towards Query Optimizer as a Service (QOaaS) in a Unified LakeHouse Ecosystem: Can One QO Rule Them All? |
2025 |
CIDR |
4.4877266e-05 |
| 8,612 |
The Case for NLP-Enhanced Database Tuning: Towards Tuning Tools that "Read the Manual" |
2021 |
VLDB |
4.4807455e-05 |
| 8,660 |
Learned Offline Query Planning via Bayesian Optimization |
2025 |
SIGMOD |
4.4680058e-05 |
| 8,714 |
Tiresias: Enabling Predictive Autonomous Storage and Indexing |
2022 |
VLDB |
4.457682e-05 |
| 8,780 |
GEqO: ML-Accelerated Semantic Equivalence Detection |
2023 |
SIGMOD |
4.4485568e-05 |
| 8,847 |
Towards Foundation Database Models |
2025 |
CIDR |
4.4329366e-05 |
| 8,961 |
T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees |
2025 |
SIGMOD |
4.4171776e-05 |
| 9,012 |
Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems |
2024 |
VLDB |
4.4059413e-05 |
| 9,032 |
Making Data Clouds Smarter at Keebo: Automated Warehouse Optimization using Data Learning |
2023 |
SIGMOD |
4.3998185e-05 |
| 9,107 |
BASE: Bridging the Gap between Cost and Latency for Query Optimization |
2023 |
VLDB |
4.3907944e-05 |
| 9,138 |
Phoebe: A Learning-based Checkpoint Optimizer |
2021 |
VLDB |
4.3842765e-05 |
| 9,141 |
Automatic SQL Error Mitigation in Oracle |
2023 |
VLDB |
4.382234e-05 |
| 9,191 |
POLAR: Adaptive and Non-invasive Join Order Selection via Plans of Least Resistance |
2024 |
VLDB |
4.3738237e-05 |
| 9,322 |
Are Joins over LSM-trees Ready? Take RocksDB as an Example |
2025 |
VLDB |
4.351469e-05 |
| 9,350 |
LIMAO: A Framework for Lifelong Modular Learned Query Optimization |
2025 |
VLDB |
4.3494621e-05 |
| 9,581 |
Low Rank Learning for Offline Query Optimization |
2025 |
SIGMOD |
4.3186744e-05 |
| 9,662 |
Efficient Query Re-optimization with Judicious Subquery Selections |
2023 |
SIGMOD |
4.3056334e-05 |
| 9,692 |
ROME: Robust Query Optimization via Parallel Multi-Plan Execution |
2024 |
SIGMOD |
4.2986161e-05 |
| 9,746 |
Still Asking: How Good Are Query Optimizers, Really? |
2025 |
VLDB |
4.2856385e-05 |
| 9,787 |
The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format |
2024 |
SIGMOD |
4.2799988e-05 |
| 9,824 |
Athena: An Effective Learning-based Framework for Query Optimizer Performance Improvement |
2025 |
SIGMOD |
4.2710095e-05 |
| 9,826 |
PLATON: Top-down R-tree Packing with Learned Partition Policy |
2023 |
SIGMOD |
4.2710095e-05 |
| 9,841 |
Machine Unlearning in Learned Databases: An Experimental Analysis |
2024 |
SIGMOD |
4.2685233e-05 |
| 9,846 |
HyperBlocker: Accelerating Rule-based Blocking in Entity Resolution using GPUs |
2025 |
VLDB |
4.2680295e-05 |
| 9,868 |
Turbo-Charging SPJ Query Plans with Learned Physical Join Operator Selections |
2022 |
VLDB |
4.2634472e-05 |
| 9,891 |
DBMS Fitting: Why should we learn what we already know? |
2020 |
CIDR |
4.2573619e-05 |
| 9,916 |
Check Out the Big Brain on BRAD: Simplifying Cloud Data Processing with Learned Automated Data Meshes |
2023 |
VLDB |
4.2520778e-05 |
| 9,931 |
Wii: Dynamic Budget Reallocation In Index Tuning |
2024 |
SIGMOD |
4.2469394e-05 |
| 9,956 |
How to Optimize SQL Queries? A Comparison Between Split, Holistic, and Hybrid Approaches |
2025 |
VLDB |
4.2332427e-05 |
| 9,959 |
An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL |
2025 |
SIGMOD |
4.2254157e-05 |
| 10,018 |
GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,087 |
High Performance or Low Memory? An Updatable Learned Index Framework for Time-Space Tradeoff |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,112 |
SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,125 |
Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis] |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,156 |
Divo: Learning a Stable and Effective Query Optimizer with a Diverse Workload |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,174 |
IDAP++: Advancing Divergence-Aware Pruning with Joint Filter and Layer Optimization |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,199 |
R2O: A Dual-Layer Framework for Joint Rewriting and Ordering in Distributed Property Graph Query Optimization |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,203 |
Reqo: A Comprehensive Learning-Based Cost Model for Robust and Explainable Query Optimization |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,219 |
Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking |
2026 |
SIGMOD |
4.1905499e-05 |