| 318 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00021167555 |
| 512 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00017050173 |
| 784 |
VerdictDB: Universalizing Approximate Query Processing |
2018 |
SIGMOD |
0.00014012614 |
| 795 |
Random Sampling over Joins Revisited |
2018 |
SIGMOD |
0.00013938779 |
| 982 |
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation |
2022 |
VLDB |
0.00012714044 |
| 1,064 |
Are We Ready For Learned Cardinality Estimation? |
2021 |
VLDB |
0.00012202282 |
| 1,082 |
Approximate Query Processing: No Silver Bullet |
2017 |
SIGMOD |
0.00012122749 |
| 1,678 |
Two-Level Sampling for Join Size Estimation |
2017 |
SIGMOD |
9.9088372e-05 |
| 1,734 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
9.7545773e-05 |
| 1,797 |
IDEBench: A Benchmark for Interactive Data Exploration |
2020 |
SIGMOD |
9.6187325e-05 |
| 2,634 |
Neural Subgraph Counting with Wasserstein Estimator |
2022 |
SIGMOD |
8.1993804e-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 |
| 2,974 |
Accurate Summary-based Cardinality Estimation Through the Lens of Cardinality Estimation Graphs |
2022 |
VLDB |
7.7938744e-05 |
| 3,160 |
A Learned Sketch for Subgraph Counting |
2021 |
SIGMOD |
7.5807496e-05 |
| 3,419 |
Revisiting Reuse for Approximate Query Processing |
2017 |
VLDB |
7.3190065e-05 |
| 3,424 |
AQP++: Connecting Approximate Query Processing With Aggregate Precomputation for Interactive Analytics |
2018 |
SIGMOD |
7.3117029e-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 |
| 5,373 |
At-the-time and Back-in-time Persistent Sketches |
2021 |
SIGMOD |
6.1569337e-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 |
| 5,738 |
Tailoring Data Source Distributions for Fairness-aware Data Integration |
2021 |
VLDB |
6.0117538e-05 |
| 5,831 |
Joins on Samples: A Theoretical Guide for Practitioners |
2020 |
VLDB |
5.9782109e-05 |
| 5,869 |
Leveraging Application Data Constraints to Optimize Database-Backed Web Applications |
2023 |
VLDB |
5.9648445e-05 |
| 5,877 |
BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees |
2019 |
SIGMOD |
5.9627218e-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 |
| 6,004 |
Approximate Query Engines: Commercial Challenges and Research Opportunities |
2017 |
SIGMOD |
5.9166815e-05 |
| 6,045 |
PathEnum: Towards Real-Time Hop-Constrained s-t Path Enumeration |
2021 |
SIGMOD |
5.9054678e-05 |
| 6,469 |
Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks |
2024 |
SIGMOD |
5.7743636e-05 |
| 6,791 |
dbET: Execution Time Distribution-based Plan Selection |
2023 |
SIGMOD |
5.6811782e-05 |
| 6,818 |
ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation |
2024 |
SIGMOD |
5.672718e-05 |
| 7,166 |
Learning to Sample: Counting with Complex Queries |
2020 |
VLDB |
5.5949741e-05 |
| 7,364 |
Weighted Distinct Sampling: Cardinality Estimation for SPJ Queries |
2021 |
SIGMOD |
5.5418075e-05 |
| 7,602 |
Scalable Approximate Butterfly and Bi-triangle Counting for Large Bipartite Networks |
2023 |
SIGMOD |
5.4868396e-05 |
| 7,604 |
Privacy Amplification by Sampling under User-level Differential Privacy |
2024 |
SIGMOD |
5.486517e-05 |
| 7,656 |
Continuous Prefetch for Interactive Data Applications |
2020 |
VLDB |
5.4772833e-05 |
| 7,701 |
Enabling Efficient and General Subpopulation Analytics in Multidimensional Data Streams |
2022 |
VLDB |
5.474978e-05 |
| 7,880 |
Identifying Insufficient Data Coverage in Databases with Multiple Relations |
2020 |
VLDB |
5.43413e-05 |
| 7,978 |
Biathlon: Harnessing Model Resilience for Accelerating ML Inference Pipelines |
2024 |
VLDB |
5.4136835e-05 |
| 8,279 |
Subset Sampling over Joins |
2026 |
PODS |
5.3637624e-05 |
| 8,283 |
Experiences with Approximating Queries in Microsoft’s Production Big-Data Clusters |
2019 |
VLDB |
5.3627138e-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 |
| 8,744 |
ProgressiveDB – Progressive Data Analytics as a Middleware |
2019 |
VLDB |
5.2881174e-05 |
| 8,850 |
SkinnerMT: Parallelizing for Efficiency and Robustness in Adaptive Query Processing on Multicore Platforms |
2023 |
VLDB |
5.2645282e-05 |
| 8,886 |
FastPDB: Towards Bag-Probabilistic Queries at Interactive Speeds |
2025 |
SIGMOD |
5.2559789e-05 |