This is an overview of the end-to-end data cleaning process. Data quality is one of the most
important problems in data management, since dirty data often leads to inaccurate data …

The generation of synthetic data can be used for anonymization, regularization,
oversampling, semi-supervised learning, self-supervised learning, and several other tasks …

We introduce a few-shot learning framework for error detection. We show that data
augmentation (a form of weak supervision) is key to training high-quality, ML-based error …

The last few years witnessed significant advances in building automated or semi-automated
data quality, data cleaning and data integration systems powered by machine learning (ML) …

Organizations are increasingly relying on data to support decisions. When data contains
private and sensitive information, the data owner often desires to publish a synthetic …

We investigate the complexity of computing an optimal repair of an inconsistent database, in
the case where integrity constraints are Functional Dependencies (FDs). We focus on two …

In this article we review the main concepts around database repairs and consistent query
answering, with emphasis on tracing back the origin, motivation, and early developments …

We study the problem of discovering functional dependencies (FD) from a noisy data set. We
adopt a statistical perspective and draw connections between FD discovery and structure …

Data cleaning is naturally framed as probabilistic inference in a generative model of ground-
truth data and likely errors, but the diversity of real-world error patterns and the hardness of …

Computing an optimal subset repair of an inconsistent database is becoming a standalone
research problem and has a wide range of applications. However, it has not been well …