| 510 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00017059914 |
| 981 |
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation |
2022 |
VLDB |
0.00012713454 |
| 1,735 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
9.7566604e-05 |
| 1,828 |
DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models |
2019 |
SIGMOD |
9.547768e-05 |
| 2,002 |
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation |
2021 |
VLDB |
9.2076835e-05 |
| 2,342 |
Learned Cardinality Estimation: An In-depth Study |
2022 |
SIGMOD |
8.6074783e-05 |
| 2,456 |
Towards Practical Oblivious Join |
2022 |
SIGMOD |
8.4350645e-05 |
| 2,518 |
Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation |
2022 |
VLDB |
8.3532841e-05 |
| 2,635 |
Neural Subgraph Counting with Wasserstein Estimator |
2022 |
SIGMOD |
8.1954989e-05 |
| 2,757 |
Answering (Unions of) Conjunctive Queries using Random Access and Random-Order Enumeration |
2020 |
PODS |
8.0487636e-05 |
| 2,824 |
G-CARE: A Framework for Performance Benchmarking of Cardinality Estimation Techniques for Subgraph Matching |
2020 |
SIGMOD |
7.9662478e-05 |
| 2,844 |
FactorJoin: A New Cardinality Estimation Framework for Join Queries |
2023 |
SIGMOD |
7.9446987e-05 |
| 3,053 |
A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation |
2021 |
SIGMOD |
7.7041081e-05 |
| 3,161 |
A Learned Sketch for Subgraph Counting |
2021 |
SIGMOD |
7.5771609e-05 |
| 3,494 |
On Join Sampling and the Hardness of Combinatorial Output-Sensitive Join Algorithms |
2023 |
PODS |
7.2548566e-05 |
| 3,742 |
FACE: A Normalizing Flow based Cardinality Estimator |
2022 |
VLDB |
7.0564546e-05 |
| 3,983 |
Simplicity Done Right for Join Ordering |
2021 |
CIDR |
6.8722161e-05 |
| 3,994 |
Guaranteeing the O~(AGM/OUT) Runtime for Uniform Sampling and Size Estimation over Joins |
2023 |
PODS |
6.8652819e-05 |
| 4,299 |
ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads |
2024 |
VLDB |
6.6766173e-05 |
| 4,459 |
Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process |
2022 |
SIGMOD |
6.5883555e-05 |
| 4,644 |
Efficient Join Synopsis Maintenance for Data Warehouse |
2020 |
SIGMOD |
6.4869417e-05 |
| 5,129 |
Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach |
2020 |
SIGMOD |
6.2582129e-05 |
| 5,236 |
FASTgres: Making Learned Query Optimizer Hinting Effective |
2023 |
VLDB |
6.2153504e-05 |
| 5,418 |
Towards Distribution-aware Query Answering in Data Markets |
2022 |
VLDB |
6.1370792e-05 |
| 5,426 |
Cardinality Estimation of Subgraph Matching: A Filtering-Sampling Approach |
2024 |
VLDB |
6.1332147e-05 |
| 5,610 |
Responsible Data Integration: Next-generation Challenges |
2022 |
SIGMOD |
6.0660772e-05 |
| 5,657 |
PGMJoins: Random Join Sampling with Graphical Models |
2021 |
SIGMOD |
6.0461e-05 |
| 5,715 |
Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data |
2023 |
SIGMOD |
6.0178585e-05 |
| 5,829 |
Joins on Samples: A Theoretical Guide for Practitioners |
2020 |
VLDB |
5.9764044e-05 |
| 5,904 |
Combining Sampling and Synopses with Worst-Case Optimal Runtime and Quality Guarantees for Graph Pattern Cardinality Estimation |
2021 |
SIGMOD |
5.9511271e-05 |
| 6,202 |
Combining Aggregation and Sampling (Nearly) Optimally for Approximate Query Processing |
2021 |
SIGMOD |
5.8494367e-05 |
| 6,472 |
Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks |
2024 |
SIGMOD |
5.7716301e-05 |
| 6,552 |
JoinBoost: Grow Trees Over Normalized Data Using Only SQL |
2023 |
VLDB |
5.7475822e-05 |
| 6,889 |
LPLM: A Neural Language Model for Cardinality Estimation of LIKE-Queries |
2024 |
SIGMOD |
5.6536655e-05 |
| 7,152 |
Learning to Sample: Counting with Complex Queries |
2020 |
VLDB |
5.5959861e-05 |
| 7,203 |
Coresets over Multiple Tables for Feature-rich and Data-efficient Machine Learning |
2023 |
VLDB |
5.5845207e-05 |
| 7,248 |
Efficient Dynamic Weighted Set Sampling and Its Extension |
2024 |
VLDB |
5.572362e-05 |
| 7,471 |
Reservoir Sampling over Joins |
2024 |
SIGMOD |
5.5172544e-05 |
| 7,518 |
Computing A Well-Representative Summary of Conjunctive Query Results |
2024 |
PODS |
5.5023404e-05 |
| 7,749 |
PilotDB: Database-Agnostic Online Approximate Query Processing with A Priori Error Guarantees |
2025 |
SIGMOD |
5.4585522e-05 |
| 8,285 |
Subset Sampling over Joins |
2026 |
PODS |
5.3612232e-05 |
| 8,351 |
alpha to omega: The Greek Alphabet of Sampling |
2020 |
CIDR |
5.3480813e-05 |
| 8,667 |
ShadowAQP: Efficient Approximate Group-by and Join Query via Attribute-oriented Sample Size Allocation and Data Generation |
2023 |
VLDB |
5.2905894e-05 |
| 8,771 |
One Size Does Not Fit All: A Bandit-Based Sampler Combination Framework with Theoretical Guarantees |
2022 |
SIGMOD |
5.2808131e-05 |
| 9,154 |
One Seed, Two Birds: A Unified Learned Structure for Exact and Approximate Counting |
2024 |
SIGMOD |
5.2176438e-05 |
| 9,642 |
Poisson Sampling over Acyclic Joins |
2026 |
SIGMOD |
5.1445295e-05 |
| 9,657 |
gSWORD: GPU-accelerated Sampling for Subgraph Counting |
2024 |
SIGMOD |
5.142891e-05 |
| 9,804 |
NeuSO: Neural Optimizer for Subgraph Queries |
2026 |
SIGMOD |
5.1233734e-05 |
| 10,157 |
Threshold Queries in Theory and in the Wild |
2022 |
VLDB |
5.0691578e-05 |
| 10,187 |
Path-centric Cardinality Estimation for Subgraph Matching |
2025 |
VLDB |
5.0628015e-05 |