Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilities

The document provides a comprehensive overview of the challenges, purposes, and evaluation of tabular data synthesis (TDS), highlighting the need for synthetic data in various domains, the complexities of TDS tools, and the development of a decision guide to assist users in selecting suitable TDS tools for specific use cases.

Abstract

In an era of rapidly advancing data-driven applications, there is a growing demand for data in both research and practice. Synthetic data have emerged as an alternative when no real data is available (e.g., due to privacy regulations). Synthesizing tabular data presents unique and complex challenges, especially handling (i) missing values, (ii) dataset imbalance, (iii) diverse column types, and (iv) complex data distributions, as well as preserving (i) column correlations, (ii) temporal dependencies, and (iii) integrity constraints (e.g., functional dependencies) present in the original dataset. While substantial progress has been made recently in the context of generational models, there is no one-size-fits-all solution for tabular data today, and choosing the right tool for a given task is therefore no trivial task. In this paper, we survey the state of the art in Tabular Data Synthesis (TDS), examine the needs of users by defining a set of functional and non-functional requirements, and compile the challenges associated with meeting those needs. In addition, we evaluate the reported performance of 36 popular research TDS tools about these requirements and develop a decision guide to help users find suitable TDS tools for their applications. The resulting decision guide also identifies significant research gaps.

Method

The authors employed a comprehensive methodology that involved assessing 36 tabular data synthesis (TDS) tools based on their suitability for specific purposes, reported performance on functional requirements, and the quality of synthetic data. They also conducted a detailed review of existing TDS approaches, including generative deep learning models, probabilistic graphical models, and database-based models. Additionally, the authors classified evaluation metrics for synthetic tabular data and developed a decision guide to assist users in selecting suitable TDS tools for their specific use cases. The methodology also involved identifying research gaps and emphasizing the need for further research in TDS tools.

Main Finding

The authors' discoveries encompassed several key findings in the field of tabular data synthesis (TDS). They evaluated 36 TDS tools based on user requirements and tool capabilities, resulting in the development of an assessment matrix and a decision guide to aid users in selecting the most suitable tool for their specific use case. Additionally, the authors identified four significant research gaps in TDS, including the need for tools that effectively preserve integrity constraints, handle complex column distributions, preserve inter-table correlations, and address all functional requirements. They also emphasized the necessity for further research to expand TDS tools or combinations of methods that ensure the preservation of integrity constraints and the capability of generating datasets with complex schemas consisting of multiple tables. Furthermore, the authors highlighted the complexity of choosing the right TDS tool in the context of data scarcity and data privacy, and emphasized the need for future research to design a benchmarking framework for evaluating TDS tools based on their fitness for diverse applications.

Conclusion

The conclusion of this paper is that the authors provided an overview of the challenges and solutions in the field of tabular data synthesis (TDS), including the identification of functional and non-functional requirements, the assessment of 36 TDS tools, and the development of a decision guide to assist users in selecting the right TDS tool for their specific use case. The authors also emphasized the need for further research to expand TDS tools and ensure the preservation of integrity constraints and the capability of generating datasets with complex schemas consisting of multiple tables. Additionally, they highlighted the complexity of choosing the right TDS tool in the context of data scarcity and data privacy and emphasized the need for a benchmarking framework to evaluate TDS tools based on their fitness for diverse applications.

Keywords

Tabular Data Synthesis, User Needs, Tool Capabilities, Probabilistic Graphical Models, Deep Learning, Generative Models, Synthetic Data Evaluation Metrics, Hybrid Models, Data Utility, Integrity Constraints, Privacy, Differential Privacy, Evaluation of Synthetic Data