One of the most critical tasks for improving data quality and increasing the reliability of data
analytics is Entity Resolution (ER), which aims to identify different descriptions that refer to …

Data quality is one of the most important problems in data management. A database system
typically aims to support the creation, maintenance and use of large amount of data …

Big data is typically characterized with 4V's: Volume, Velocity, Variety and Veracity. When it
comes to big graphs, these challenges become even more staggering. Each and every of …

Data-cleaning (or data-repairing) is considered a crucial problem in many database-related
tasks. It consists in making a database consistent with respect to a set of given constraints. In …

Recently, there has been a renovated interest in functional dependencies due to the
possibility of employing them in several advanced database operations, such as data …

A variety of integrity constraints have been studied for data cleaning. While these constraints
can detect the presence of errors, they fall short of guiding us to correct the errors. Indeed …

Data quantity and data quality, like two sides of a coin, are equally important to data
management. This paper provides an overview of recent advances in the study of data …

Central to a data cleaning system are record matching and data repairing. Matching aims to
identify tuples that refer to the same real-world object, and repairing is to make a database …

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In this work, we provide some insights and develop some ideas, with few technical details,
about the role of explanations in Data Quality in the context of data-based machine learning …