| 2,349 |
The Dawn of Natural Language to SQL: Are We Fully Ready? |
2024 |
VLDB |
22 |
8.5969105e-05 |
| 2,393 |
A Learned Query Rewrite System using Monte Carlo Tree Search |
2022 |
VLDB |
37 |
8.5298464e-05 |
| 2,483 |
AnalyticDB: Real-time OLAP Database System at Alibaba Cloud |
2019 |
VLDB |
36 |
8.3973995e-05 |
| 2,686 |
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection |
2022 |
VLDB |
36 |
8.1300913e-05 |
| 3,742 |
FACE: A Normalizing Flow based Cardinality Estimator |
2022 |
VLDB |
32 |
7.0564546e-05 |
| 3,942 |
GoodCore: Data-effective and Data-efficient Machine Learning through Coreset Selection over Incomplete Data |
2023 |
SIGMOD |
13 |
6.9105312e-05 |
| 4,411 |
LakeBench: A Benchmark for Discovering Joinable and Unionable Tables in Data Lakes |
2024 |
VLDB |
10 |
6.6089506e-05 |
| 4,485 |
LearnedSQLGen: Constraint-aware SQL Generation using Reinforcement Learning |
2022 |
SIGMOD |
12 |
6.5771335e-05 |
| 4,565 |
QUEST: Query Optimization in Unstructured Document Analysis |
2025 |
VLDB |
9 |
6.5295131e-05 |
| 4,625 |
Cost-Effective Crowdsourced Entity Resolution: A Partial-Order Approach |
2016 |
SIGMOD |
26 |
6.4948389e-05 |
| 4,865 |
Synthesizing Natural Language to Visualization (NL2VIS) Benchmarks from NL2SQL Benchmarks |
2021 |
SIGMOD |
14 |
6.3740341e-05 |
| 4,869 |
Selective Data Acquisition in the Wild for Model Charging |
2022 |
VLDB |
18 |
6.3719187e-05 |
| 5,050 |
CDB: A Crowd-Powered Database System |
2018 |
VLDB |
10 |
6.2955765e-05 |
| 5,815 |
Domain Adaptation for Deep Entity Resolution |
2022 |
SIGMOD |
13 |
5.9824897e-05 |
| 5,851 |
HAIPipe: Combining Human-generated and Machine-generated Pipelines for Data Preparation |
2023 |
SIGMOD |
10 |
5.9692535e-05 |
| 6,159 |
Automatic Data Acquisition for Deep Learning |
2021 |
VLDB |
7 |
5.8653048e-05 |
| 6,874 |
Doctopus: Budget-aware Structural Table Extraction from Unstructured Documents |
2025 |
VLDB |
5 |
5.6572257e-05 |
| 7,043 |
Human-in-the-loop Outlier Detection |
2020 |
SIGMOD |
17 |
5.611642e-05 |
| 7,092 |
LEAD: Iterative Data Selection for Efficient LLM Instruction Tuning |
2026 |
VLDB |
2 |
5.5991152e-05 |
| 7,203 |
Coresets over Multiple Tables for Feature-rich and Data-efficient Machine Learning |
2023 |
VLDB |
8 |
5.5845207e-05 |
| 7,262 |
Learned Data-aware Image Representations of Line Charts for Similarity Search |
2023 |
SIGMOD |
7 |
5.5694763e-05 |
| 7,589 |
Data Imputation with Limited Data Redundancy Using Data Lakes |
2025 |
VLDB |
4 |
5.4880216e-05 |
| 7,654 |
MisDetect: Iterative Mislabel Detection using Early Loss |
2024 |
VLDB |
5 |
5.4746904e-05 |
| 7,771 |
LakeCompass: An End-to-End System for Data Maintenance, Search and Analysis in Data Lakes |
2024 |
VLDB |
2 |
5.453953e-05 |
| 8,182 |
DocDB: A Database for Unstructured Document Analysis |
2025 |
VLDB |
3 |
5.3810756e-05 |
| 8,483 |
DADER: Hands-Off Entity Resolution with Domain Adaptation |
2022 |
VLDB |
3 |
5.3311145e-05 |
| 8,688 |
Unstructured Data Analysis Using LLMs: A Comprehensive Benchmark |
2026 |
VLDB |
1 |
5.2880532e-05 |
| 8,808 |
PACE: Poisoning Attacks on Learned Cardinality Estimation |
2024 |
SIGMOD |
4 |
5.2717563e-05 |
| 9,220 |
Natural Language to SQL: State of the Art and Open Problems |
2025 |
VLDB |
6 |
5.2036865e-05 |
| 9,481 |
VisClean: Interactive Cleaning for Progressive Visualization |
2020 |
VLDB |
8 |
5.1689938e-05 |
| 9,591 |
CDB: Optimizing Queries with Crowd-Based Selections and Joins |
2017 |
SIGMOD |
9 |
5.154741e-05 |
| 10,721 |
BRIEF: Bi-level Coreset Selection for Efficient Instruction Tuning in LLMs |
2026 |
VLDB |
0 |
4.9769913e-05 |
| 10,831 |
Data-efficient Online Training for Direct Alignment in LLMs |
2026 |
VLDB |
0 |
4.9769913e-05 |
| 10,847 |
EcoTable: Cost-effective Table Integration in Data Lakes for Natural Language Queries |
2026 |
VLDB |
0 |
4.9769913e-05 |
| 11,222 |
Two Birds with One Stone: Efficient Deep Learning over Mislabeled Data through Subset Selection |
2025 |
SIGMOD |
1 |
4.9769913e-05 |
| 12,086 |
Interactively Discovering and Ranking Desired Tuples without Writing SQL Queries |
2020 |
SIGMOD |
3 |
4.9769913e-05 |