Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

Detecting and repairing dirty data is one of the perennial challenges in data analytics, and
failure to do so can result in inaccurate analytics and unreliable decisions. Over the past few …

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 …

Integrity constraints (ICs) provide a valuable tool for enforcing correct application semantics.
However, designing ICs requires experts and time. Proposals for automatic discovery have …

Data quality affects machine learning (ML) model performances, and data scientists spend
considerable amount of time on data cleaning before model training. However, to date, there …

We propose a class of integrity constraints for relational databases, referred to as conditional
functional dependencies (CFDs), and study their applications in data cleaning. In contrast to …