Summary of Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilities, by Maria F. Davila R. and Sven Groen and Fabian Panse and Wolfram Wingerath
Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilities
by Maria F. Davila R., Sven Groen, Fabian Panse, Wolfram Wingerath
First submitted to arxiv on: 31 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Databases (cs.DB)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the challenges and opportunities in synthetic data generation for tabular data, which is crucial in various applications where real data may not be available due to privacy concerns or other reasons. The authors identify four key complexities: missing values, dataset imbalance, diverse column types, and complex data distributions. They also highlight the importance of preserving correlations, temporal dependencies, and integrity constraints present in the original dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Synthetic data can help bridge the gap between available data and what’s needed for research or practical applications. The paper reviews the current state-of-the-art methods for tabular data synthesis (TDS), defines functional and non-functional requirements for users, and identifies challenges associated with meeting these needs. Additionally, the authors evaluate the performance of 36 popular TDS tools against these requirements and develop a decision guide to help users find suitable tools. |
Keywords
» Artificial intelligence » Synthetic data
