Data Collection and Quality Challenges for Deep Learning
Summary: Examines data collection and quality challenges in deep learning, emphasizing data as a first-class citizen and data prep costs dominating DL workflows. It surveys collection, validation/cleaning techniques, and robust/fair training to handle bias and errors, urging data-management leadership. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Steven Euijong Whang (Korea Advanced Institute of Science and Technology)
- 2. Jae-Gil Lee (Korea Advanced Institute of Science and Technology)
BibTeX Citation
@article{whang_vldb20,
title = {{Data Collection and Quality Challenges for Deep Learning}},
author = {Whang, Steven Euijong and Lee, Jae-Gil},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {12},
pages = {3429--3432},
doi = {10.14778/3415478.3415562},
url = {https://doi.org/10.14778/3415478.3415562},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,510 | Responsible Data Integration: Next-generation Challenges | 2022 | SIGMOD | 6.1919207e-05 |
| 7,844 | Missing Value Imputation for Multi-attribute Sensor Data Streams via Message Propagation | 2024 | VLDB | 5.5332653e-05 |
| 9,243 | Scapin: Scalable Graph Structure Perturbation by Augmented Influence Maximization | 2023 | SIGMOD | 5.2993405e-05 |
| 9,930 | Data Augmentation for ML-driven Data Preparation and Integration | 2021 | VLDB | 5.1955087e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 410 | SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics | 2015 | VLDB | 0.0001890421 |
| 749 | Data Lake Management: Challenges and Opportunities | 2019 | VLDB | 0.00014379989 |
| 1,147 | Data Management Challenges in Production Machine Learning | 2017 | SIGMOD | 0.00011974846 |
| 1,250 | Data Management in Machine Learning: Challenges, Techniques, and Systems | 2017 | SIGMOD | 0.00011485301 |
| 3,258 | Controlling False Discoveries During Interactive Data Exploration | 2017 | SIGMOD | 7.5897862e-05 |
| 7,424 | Data Integration and Machine Learning: A Natural Synergy | 2018 | VLDB | 5.6214566e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 5,510 | Responsible Data Integration: Next-generation Challenges | 2022 | SIGMOD |
| 2 | 6,392 | Qualitative Data Cleaning | 2016 | VLDB |
| 3 | 4,859 | Machine Learning for Big Data | 2013 | SIGMOD |
| 4 | 7,661 | Machine Learning for Cloud Data Systems: the Progress so far and the Path Forward | 2021 | VLDB |
| 5 | 8,328 | Deep Learning: Systems and Responsibility | 2021 | SIGMOD |
| 6 | 1,484 | Data Quality and Data Cleaning: An Overview | 2003 | SIGMOD |
| 7 | 13,447 | Deep Data Integration | 2021 | SIGMOD |
| 8 | 1,323 | Data Cleaning: Overview and Emerging Challenges | 2016 | SIGMOD |
| 9 | 1,147 | Data Management Challenges in Production Machine Learning | 2017 | SIGMOD |
| 10 | 1,250 | Data Management in Machine Learning: Challenges, Techniques, and Systems | 2017 | SIGMOD |