Web data have grown exponentially to reach zettabyte scales. Mountains of data come from
several online applications, such as e-commerce, social media, web and sensor-based …

This paper provides a comprehensive description of the current literature on data fusion,
with an emphasis on Information Quality (IQ) and performance evaluation. This literature …

The application of machine learning techniques to large and distributed data archives might
result in the disclosure of sensitive information about the data subjects. Data often contain …

Nowadays, new laws and regulations, such as the European General Data Protection
Regulation (GDPR), require companies to define privacy policies complying with the …

We study the problem of self-supervised and interpretable data cleaning, which
automatically extracts interpretable data repair rules from dirty data. In this paper, we …

We propose the first framework for discovering the set of meaningful functional
dependencies from data. This set contains the true positives among the set of functional …

One of the main challenges in data profiling is to efficiently extract metadata from dynamic
information sources, by avoiding the processing of the whole dataset from scratch upon …

Data stream profiling concerns the automatic extraction of metadata from a data stream,
without having the possibility to store it. Among the metadata of interest, functional …

Differential dependencies (DDs) are proposed to specify constraints on the differences
between values, where the semantics of difference can be" similar"," dissimilar" and beyond …

Assessing data quality through Functional Depen-dencies (FDs) is a crucial aspect of data
governance. However, with the diverse range of data sources and the exponential growth in …