| 318 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00021166957 |
| 510 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00017059914 |
| 772 |
VerdictDB: Universalizing Approximate Query Processing |
2018 |
SIGMOD |
0.0001409096 |
| 795 |
Random Sampling over Joins Revisited |
2018 |
SIGMOD |
0.00013934719 |
| 981 |
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation |
2022 |
VLDB |
0.00012713454 |
| 1,061 |
Approximate Query Processing: No Silver Bullet |
2017 |
SIGMOD |
0.00012208639 |
| 1,065 |
Are We Ready For Learned Cardinality Estimation? |
2021 |
VLDB |
0.00012202293 |
| 1,678 |
Two-Level Sampling for Join Size Estimation |
2017 |
SIGMOD |
9.9056116e-05 |
| 1,735 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
9.7566604e-05 |
| 1,797 |
IDEBench: A Benchmark for Interactive Data Exploration |
2020 |
SIGMOD |
9.6143465e-05 |
| 2,635 |
Neural Subgraph Counting with Wasserstein Estimator |
2022 |
SIGMOD |
8.1954989e-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 |
| 2,975 |
Accurate Summary-based Cardinality Estimation Through the Lens of Cardinality Estimation Graphs |
2022 |
VLDB |
7.7905662e-05 |
| 3,161 |
A Learned Sketch for Subgraph Counting |
2021 |
SIGMOD |
7.5771609e-05 |
| 3,417 |
Revisiting Reuse for Approximate Query Processing |
2017 |
VLDB |
7.3184905e-05 |
| 3,424 |
AQP++: Connecting Approximate Query Processing With Aggregate Precomputation for Interactive Analytics |
2018 |
SIGMOD |
7.3084429e-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 |
| 5,379 |
At-the-time and Back-in-time Persistent Sketches |
2021 |
SIGMOD |
6.1540191e-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 |
| 5,739 |
Tailoring Data Source Distributions for Fairness-aware Data Integration |
2021 |
VLDB |
6.0089081e-05 |
| 5,829 |
Joins on Samples: A Theoretical Guide for Practitioners |
2020 |
VLDB |
5.9764044e-05 |
| 5,870 |
Leveraging Application Data Constraints to Optimize Database-Backed Web Applications |
2023 |
VLDB |
5.9620208e-05 |
| 5,877 |
BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees |
2019 |
SIGMOD |
5.9599042e-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 |
| 6,002 |
Approximate Query Engines: Commercial Challenges and Research Opportunities |
2017 |
SIGMOD |
5.9149725e-05 |
| 6,047 |
PathEnum: Towards Real-Time Hop-Constrained s-t Path Enumeration |
2021 |
SIGMOD |
5.9026722e-05 |
| 6,472 |
Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks |
2024 |
SIGMOD |
5.7716301e-05 |
| 6,796 |
dbET: Execution Time Distribution-based Plan Selection |
2023 |
SIGMOD |
5.6784895e-05 |
| 6,824 |
ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation |
2024 |
SIGMOD |
5.670071e-05 |
| 7,152 |
Learning to Sample: Counting with Complex Queries |
2020 |
VLDB |
5.5959861e-05 |
| 7,368 |
Weighted Distinct Sampling: Cardinality Estimation for SPJ Queries |
2021 |
SIGMOD |
5.5392867e-05 |
| 7,608 |
Scalable Approximate Butterfly and Bi-triangle Counting for Large Bipartite Networks |
2023 |
SIGMOD |
5.4842422e-05 |
| 7,610 |
Privacy Amplification by Sampling under User-level Differential Privacy |
2024 |
SIGMOD |
5.4839197e-05 |
| 7,662 |
Continuous Prefetch for Interactive Data Applications |
2020 |
VLDB |
5.4746904e-05 |
| 7,707 |
Enabling Efficient and General Subpopulation Analytics in Multidimensional Data Streams |
2022 |
VLDB |
5.4723863e-05 |
| 7,885 |
Identifying Insufficient Data Coverage in Databases with Multiple Relations |
2020 |
VLDB |
5.4315576e-05 |
| 7,983 |
Biathlon: Harnessing Model Resilience for Accelerating ML Inference Pipelines |
2024 |
VLDB |
5.4111208e-05 |
| 8,285 |
Subset Sampling over Joins |
2026 |
PODS |
5.3612232e-05 |
| 8,289 |
Experiences with Approximating Queries in Microsoft’s Production Big-Data Clusters |
2019 |
VLDB |
5.360349e-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 |
| 8,749 |
ProgressiveDB – Progressive Data Analytics as a Middleware |
2019 |
VLDB |
5.2865484e-05 |
| 8,860 |
SkinnerMT: Parallelizing for Efficiency and Robustness in Adaptive Query Processing on Multicore Platforms |
2023 |
VLDB |
5.2620362e-05 |
| 8,895 |
FastPDB: Towards Bag-Probabilistic Queries at Interactive Speeds |
2025 |
SIGMOD |
5.2534908e-05 |