| 2,117 |
The Dawn of Natural Language to SQL: Are We Fully Ready? |
2024 |
VLDB |
24 |
9.0012524e-05 |
| 2,389 |
A Learned Query Rewrite System using Monte Carlo Tree Search |
2022 |
VLDB |
37 |
8.5228689e-05 |
| 2,474 |
AnalyticDB: Real-time OLAP Database System at Alibaba Cloud |
2019 |
VLDB |
36 |
8.3955114e-05 |
| 2,696 |
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection |
2022 |
VLDB |
36 |
8.1068354e-05 |
| 3,740 |
FACE: A Normalizing Flow based Cardinality Estimator |
2022 |
VLDB |
32 |
7.0398782e-05 |
| 3,757 |
GoodCore: Data-effective and Data-efficient Machine Learning through Coreset Selection over Incomplete Data |
2023 |
SIGMOD |
14 |
7.0254733e-05 |
| 4,345 |
Synthesizing Natural Language to Visualization (NL2VIS) Benchmarks from NL2SQL Benchmarks |
2021 |
SIGMOD |
15 |
6.6388774e-05 |
| 4,419 |
LakeBench: A Benchmark for Discovering Joinable and Unionable Tables in Data Lakes |
2024 |
VLDB |
10 |
6.5905711e-05 |
| 4,508 |
LearnedSQLGen: Constraint-aware SQL Generation using Reinforcement Learning |
2022 |
SIGMOD |
12 |
6.5553854e-05 |
| 4,583 |
QUEST: Query Optimization in Unstructured Document Analysis |
2025 |
VLDB |
9 |
6.5079225e-05 |
| 4,626 |
Cost-Effective Crowdsourced Entity Resolution: A Partial-Order Approach |
2016 |
SIGMOD |
26 |
6.480594e-05 |
| 4,890 |
Selective Data Acquisition in the Wild for Model Charging |
2022 |
VLDB |
18 |
6.3508492e-05 |
| 5,051 |
CDB: A Crowd-Powered Database System |
2018 |
VLDB |
10 |
6.2863221e-05 |
| 5,466 |
HAIPipe: Combining Human-generated and Machine-generated Pipelines for Data Preparation |
2023 |
SIGMOD |
11 |
6.1038307e-05 |
| 5,830 |
Domain Adaptation for Deep Entity Resolution |
2022 |
SIGMOD |
13 |
5.9627079e-05 |
| 6,173 |
Automatic Data Acquisition for Deep Learning |
2021 |
VLDB |
7 |
5.8459104e-05 |
| 6,654 |
Coresets over Multiple Tables for Feature-rich and Data-efficient Machine Learning |
2023 |
VLDB |
9 |
5.7038474e-05 |
| 6,893 |
Doctopus: Budget-aware Structural Table Extraction from Unstructured Documents |
2025 |
VLDB |
5 |
5.6385194e-05 |
| 7,064 |
Human-in-the-loop Outlier Detection |
2020 |
SIGMOD |
17 |
5.5930864e-05 |
| 7,115 |
LEAD: Iterative Data Selection for Efficient LLM Instruction Tuning |
2026 |
VLDB |
2 |
5.580601e-05 |
| 7,281 |
Learned Data-aware Image Representations of Line Charts for Similarity Search |
2023 |
SIGMOD |
7 |
5.5510601e-05 |
| 7,606 |
Data Imputation with Limited Data Redundancy Using Data Lakes |
2025 |
VLDB |
4 |
5.4698748e-05 |
| 7,667 |
MisDetect: Iterative Mislabel Detection using Early Loss |
2024 |
VLDB |
5 |
5.4565877e-05 |
| 7,785 |
LakeCompass: An End-to-End System for Data Maintenance, Search and Analysis in Data Lakes |
2024 |
VLDB |
2 |
5.4359188e-05 |
| 8,096 |
Two Birds with One Stone: Efficient Deep Learning over Mislabeled Data through Subset Selection |
2025 |
SIGMOD |
3 |
5.3739121e-05 |
| 8,198 |
DocDB: A Database for Unstructured Document Analysis |
2025 |
VLDB |
3 |
5.3632824e-05 |
| 8,498 |
DADER: Hands-Off Entity Resolution with Domain Adaptation |
2022 |
VLDB |
3 |
5.3134865e-05 |
| 8,708 |
Unstructured Data Analysis Using LLMs: A Comprehensive Benchmark |
2026 |
VLDB |
1 |
5.2705676e-05 |
| 8,825 |
PACE: Poisoning Attacks on Learned Cardinality Estimation |
2024 |
SIGMOD |
4 |
5.2543246e-05 |
| 9,242 |
Natural Language to SQL: State of the Art and Open Problems |
2025 |
VLDB |
6 |
5.1864799e-05 |
| 9,510 |
VisClean: Interactive Cleaning for Progressive Visualization |
2020 |
VLDB |
8 |
5.1519019e-05 |
| 9,617 |
CDB: Optimizing Queries with Crowd-Based Selections and Joins |
2017 |
SIGMOD |
9 |
5.1376962e-05 |
| 10,782 |
BRIEF: Bi-level Coreset Selection for Efficient Instruction Tuning in LLMs |
2026 |
VLDB |
0 |
4.9605343e-05 |
| 10,892 |
Data-efficient Online Training for Direct Alignment in LLMs |
2026 |
VLDB |
0 |
4.9605343e-05 |
| 10,908 |
EcoTable: Cost-effective Table Integration in Data Lakes for Natural Language Queries |
2026 |
VLDB |
0 |
4.9605343e-05 |
| 12,130 |
Interactively Discovering and Ranking Desired Tuples without Writing SQL Queries |
2020 |
SIGMOD |
3 |
4.9605343e-05 |