| 512 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00017050173 |
| 982 |
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation |
2022 |
VLDB |
0.00012714044 |
| 1,734 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
9.7545773e-05 |
| 1,829 |
DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models |
2019 |
SIGMOD |
9.5510333e-05 |
| 2,004 |
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation |
2021 |
VLDB |
9.2065719e-05 |
| 2,342 |
Learned Cardinality Estimation: An In-depth Study |
2022 |
SIGMOD |
8.6060437e-05 |
| 2,455 |
Towards Practical Oblivious Join |
2022 |
SIGMOD |
8.4390594e-05 |
| 2,522 |
Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation |
2022 |
VLDB |
8.3477168e-05 |
| 2,634 |
Neural Subgraph Counting with Wasserstein Estimator |
2022 |
SIGMOD |
8.1993804e-05 |
| 2,757 |
Answering (Unions of) Conjunctive Queries using Random Access and Random-Order Enumeration |
2020 |
PODS |
8.0525756e-05 |
| 2,824 |
G-CARE: A Framework for Performance Benchmarking of Cardinality Estimation Techniques for Subgraph Matching |
2020 |
SIGMOD |
7.9698957e-05 |
| 2,846 |
FactorJoin: A New Cardinality Estimation Framework for Join Queries |
2023 |
SIGMOD |
7.9453616e-05 |
| 3,052 |
A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation |
2021 |
SIGMOD |
7.7052471e-05 |
| 3,160 |
A Learned Sketch for Subgraph Counting |
2021 |
SIGMOD |
7.5807496e-05 |
| 3,494 |
On Join Sampling and the Hardness of Combinatorial Output-Sensitive Join Algorithms |
2023 |
PODS |
7.2582926e-05 |
| 3,741 |
FACE: A Normalizing Flow based Cardinality Estimator |
2022 |
VLDB |
7.0594076e-05 |
| 3,982 |
Simplicity Done Right for Join Ordering |
2021 |
CIDR |
6.8750228e-05 |
| 3,992 |
Guaranteeing the O~(AGM/OUT) Runtime for Uniform Sampling and Size Estimation over Joins |
2023 |
PODS |
6.8685334e-05 |
| 4,311 |
ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads |
2024 |
VLDB |
6.6727978e-05 |
| 4,457 |
Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process |
2022 |
SIGMOD |
6.5913732e-05 |
| 4,642 |
Efficient Join Synopsis Maintenance for Data Warehouse |
2020 |
SIGMOD |
6.4898745e-05 |
| 5,126 |
Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach |
2020 |
SIGMOD |
6.261175e-05 |
| 5,241 |
FASTgres: Making Learned Query Optimizer Hinting Effective |
2023 |
VLDB |
6.2154384e-05 |
| 5,414 |
Towards Distribution-aware Query Answering in Data Markets |
2022 |
VLDB |
6.1399858e-05 |
| 5,422 |
Cardinality Estimation of Subgraph Matching: A Filtering-Sampling Approach |
2024 |
VLDB |
6.1361195e-05 |
| 5,609 |
Responsible Data Integration: Next-generation Challenges |
2022 |
SIGMOD |
6.0689501e-05 |
| 5,657 |
PGMJoins: Random Join Sampling with Graphical Models |
2021 |
SIGMOD |
6.0488437e-05 |
| 5,716 |
Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data |
2023 |
SIGMOD |
6.0194657e-05 |
| 5,831 |
Joins on Samples: A Theoretical Guide for Practitioners |
2020 |
VLDB |
5.9782109e-05 |
| 5,901 |
Combining Sampling and Synopses with Worst-Case Optimal Runtime and Quality Guarantees for Graph Pattern Cardinality Estimation |
2021 |
SIGMOD |
5.9539374e-05 |
| 6,221 |
Combining Aggregation and Sampling (Nearly) Optimally for Approximate Query Processing |
2021 |
SIGMOD |
5.8463347e-05 |
| 6,469 |
Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks |
2024 |
SIGMOD |
5.7743636e-05 |
| 6,550 |
JoinBoost: Grow Trees Over Normalized Data Using Only SQL |
2023 |
VLDB |
5.7503043e-05 |
| 6,884 |
LPLM: A Neural Language Model for Cardinality Estimation of LIKE-Queries |
2024 |
SIGMOD |
5.6563432e-05 |
| 7,166 |
Learning to Sample: Counting with Complex Queries |
2020 |
VLDB |
5.5949741e-05 |
| 7,201 |
Coresets over Multiple Tables for Feature-rich and Data-efficient Machine Learning |
2023 |
VLDB |
5.5871656e-05 |
| 7,246 |
Efficient Dynamic Weighted Set Sampling and Its Extension |
2024 |
VLDB |
5.5750011e-05 |
| 7,467 |
Reservoir Sampling over Joins |
2024 |
SIGMOD |
5.5198675e-05 |
| 7,513 |
Computing A Well-Representative Summary of Conjunctive Query Results |
2024 |
PODS |
5.5049463e-05 |
| 8,223 |
PilotDB: Database-Agnostic Online Approximate Query Processing with A Priori Error Guarantees |
2025 |
SIGMOD |
5.3751366e-05 |
| 8,279 |
Subset Sampling over Joins |
2026 |
PODS |
5.3637624e-05 |
| 8,347 |
alpha to omega: The Greek Alphabet of Sampling |
2020 |
CIDR |
5.350539e-05 |
| 8,659 |
ShadowAQP: Efficient Approximate Group-by and Join Query via Attribute-oriented Sample Size Allocation and Data Generation |
2023 |
VLDB |
5.2930951e-05 |
| 8,770 |
One Size Does Not Fit All: A Bandit-Based Sampler Combination Framework with Theoretical Guarantees |
2022 |
SIGMOD |
5.2812395e-05 |
| 9,145 |
One Seed, Two Birds: A Unified Learned Structure for Exact and Approximate Counting |
2024 |
SIGMOD |
5.220115e-05 |
| 9,634 |
Poisson Sampling over Acyclic Joins |
2026 |
SIGMOD |
5.146966e-05 |
| 9,650 |
gSWORD: GPU-accelerated Sampling for Subgraph Counting |
2024 |
SIGMOD |
5.1453267e-05 |
| 9,797 |
NeuSO: Neural Optimizer for Subgraph Queries |
2026 |
SIGMOD |
5.1257999e-05 |
| 10,153 |
Threshold Queries in Theory and in the Wild |
2022 |
VLDB |
5.0715586e-05 |
| 10,184 |
Path-centric Cardinality Estimation for Subgraph Matching |
2025 |
VLDB |
5.0651993e-05 |