| 7,011 |
Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis |
2023 |
VLDB |
4.8629458e-05 |
| 7,034 |
A Neural Database for Differentially Private Spatial Range Queries |
2022 |
VLDB |
4.8550912e-05 |
| 7,123 |
ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation |
2024 |
SIGMOD |
4.8251036e-05 |
| 7,126 |
Debunking the Myth of Join Ordering: Toward Robust SQL Analytics |
2025 |
SIGMOD |
4.8232367e-05 |
| 7,186 |
LPLM: A Neural Language Model for Cardinality Estimation of LIKE-Queries |
2024 |
SIGMOD |
4.8063731e-05 |
| 7,221 |
Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation |
2023 |
SIGMOD |
4.797194e-05 |
| 7,336 |
Refactoring Index Tuning Process with Benefit Estimation |
2024 |
VLDB |
4.7599411e-05 |
| 7,457 |
Selectivity Functions of Range Queries are Learnable* |
2022 |
SIGMOD |
4.7247191e-05 |
| 7,467 |
Yannakakis+: Practical Acyclic Query Evaluation with Theoretical Guarantees |
2025 |
SIGMOD |
4.7218691e-05 |
| 7,474 |
Cardinality Estimation of Approximate Substring Queries using Deep Learning |
2022 |
VLDB |
4.7194345e-05 |
| 7,610 |
Learning to be a Statistician: Learned Estimator for Number of Distinct Values |
2022 |
VLDB |
4.6965039e-05 |
| 7,634 |
ReStore - Neural Data Completion for Relational Databases |
2021 |
SIGMOD |
4.6911382e-05 |
| 7,753 |
Rethinking Learned Cost Models: Why Start from Scratch? |
2023 |
SIGMOD |
4.660151e-05 |
| 7,828 |
Modeling Shifting Workloads for Learned Database Systems |
2024 |
SIGMOD |
4.6407986e-05 |
| 7,854 |
dbET: Execution Time Distribution-based Plan Selection |
2023 |
SIGMOD |
4.6350172e-05 |
| 8,009 |
CAMAL: Optimizing LSM-trees via Active Learning |
2024 |
SIGMOD |
4.6066863e-05 |
| 8,020 |
The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions |
2024 |
VLDB |
4.6040862e-05 |
| 8,080 |
Biathlon: Harnessing Model Resilience for Accelerating ML Inference Pipelines |
2024 |
VLDB |
4.5911668e-05 |
| 8,220 |
PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! |
2021 |
VLDB |
4.5557328e-05 |
| 8,393 |
LAQy: Efficient and Reusable Query Approximations via Lazy Sampling |
2023 |
SIGMOD |
4.5280102e-05 |
| 8,650 |
HAP: An Efficient Hamming Space Index Based on Augmented Pigeonhole Principle |
2022 |
SIGMOD |
4.4761716e-05 |
| 8,680 |
A Practical Approach to Groupjoin and Nested Aggregates |
2021 |
VLDB |
4.4694927e-05 |
| 8,697 |
Convolution and Cross-Correlation of Count Sketches Enables Fast Cardinality Estimation of Multi-Join Queries |
2024 |
SIGMOD |
4.4657888e-05 |
| 8,834 |
ByteCard: Enhancing ByteDance’s Data Warehouse with Learned Cardinality Estimation |
2024 |
SIGMOD |
4.4394021e-05 |
| 8,847 |
Towards Foundation Database Models |
2025 |
CIDR |
4.4371897e-05 |
| 8,854 |
Optimizing the cloud? Don't train models. Build oracles! |
2024 |
CIDR |
4.4349047e-05 |
| 8,948 |
One Seed, Two Birds: A Unified Learned Structure for Exact and Approximate Counting |
2024 |
SIGMOD |
4.423786e-05 |
| 8,956 |
T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees |
2025 |
SIGMOD |
4.4214154e-05 |
| 9,006 |
Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems |
2024 |
VLDB |
4.4101482e-05 |
| 9,107 |
NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks |
2023 |
SIGMOD |
4.3950706e-05 |
| 9,194 |
Phoebe: A Learning-based Checkpoint Optimizer |
2021 |
VLDB |
4.3761777e-05 |
| 9,213 |
PACE: Poisoning Attacks on Learned Cardinality Estimation |
2024 |
SIGMOD |
4.3721075e-05 |
| 9,317 |
Are Joins over LSM-trees Ready? Take RocksDB as an Example |
2025 |
VLDB |
4.3556432e-05 |
| 9,431 |
PairwiseHist: Fast, Accurate and Space-Efficient Approximate Query Processing with Data Compression |
2024 |
VLDB |
4.3434046e-05 |
| 9,621 |
ShadowAQP: Efficient Approximate Group-by and Join Query via Attribute-oriented Sample Size Allocation and Data Generation |
2023 |
VLDB |
4.3167167e-05 |
| 9,628 |
Approximate Sketches |
2024 |
SIGMOD |
4.3143499e-05 |
| 9,662 |
Efficient Query Re-optimization with Judicious Subquery Selections |
2023 |
SIGMOD |
4.3097631e-05 |
| 9,691 |
Selectivity Estimation for Queries Containing Predicates over Set-Valued Attributes |
2023 |
SIGMOD |
4.3035354e-05 |
| 9,693 |
ROME: Robust Query Optimization via Parallel Multi-Plan Execution |
2024 |
SIGMOD |
4.3027391e-05 |
| 9,726 |
Cardinality Estimation of LIKE Predicate Queries using Deep Learning |
2025 |
SIGMOD |
4.2943379e-05 |
| 9,747 |
Still Asking: How Good Are Query Optimizers, Really? |
2025 |
VLDB |
4.2897489e-05 |
| 9,757 |
Efficient Insights Discovery through Conditional Generative Model based Query Approximation |
2022 |
SIGMOD |
4.2893233e-05 |
| 9,812 |
A Practical Theory of Generalization in Selectivity Learning |
2025 |
VLDB |
4.2783272e-05 |
| 9,825 |
Athena: An Effective Learning-based Framework for Query Optimizer Performance Improvement |
2025 |
SIGMOD |
4.2751057e-05 |
| 9,852 |
Machine Unlearning in Learned Databases: An Experimental Analysis |
2024 |
SIGMOD |
4.2714575e-05 |
| 9,869 |
Turbo-Charging SPJ Query Plans with Learned Physical Join Operator Selections |
2022 |
VLDB |
4.2675361e-05 |
| 9,878 |
PRICE: A Pretrained Model for Cross-Database Cardinality Estimation |
2025 |
VLDB |
4.2656547e-05 |
| 9,917 |
Check Out the Big Brain on BRAD: Simplifying Cloud Data Processing with Learned Automated Data Meshes |
2023 |
VLDB |
4.2561557e-05 |
| 9,945 |
SSCard: Substring Cardinality Estimation using Suffix Tree-Guided Learned FM-Index |
2026 |
SIGMOD |
4.2432653e-05 |
| 9,960 |
An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL |
2025 |
SIGMOD |
4.2294678e-05 |