Relational Data Cleaning Meets Artificial Intelligence: A Survey

Abstract Relational data play a crucial role in various fields, but they are often plagued by low-quality issues such as erroneous and missing values, which can terribly impact downstream applications. To tackle these issues, relational data cleaning with traditional signals, e.g., statistics, constraints, and clusters, have been extensively studied, with interpretability and efficiency. Recently, considering the strong capability of modeling complex relationships, artificial intelligence (AI) techniques have been introduced into the data cleaning field. These AI-based methods either consider multiple cleaning signals, integrate various techniques into the cleaning system, or incorporate neural networks. Among them, methods utilizing deep neural networks are classified as deep learning (DL) based, while those that do not are classified as machine learning (ML) based. In this study, we focus on three essential tasks (i.e., error detection, data repairing, and data imputation) for cleaning relational data, to comprehensively review the representative methods using traditional or AI techniques. By comparing and analyzing two types of methods across five dimensions (cost, generalization, interpretability, efficiency, and effectiveness), we provide insights into their strengths, weaknesses, and suitable application scenarios. Finally, we analyze the challenges and open issues currently faced in data cleaning and discuss possible directions for future studies.