Due to the ubiquity of missing data, data imputation has received extensive attention in the
past decades. It is a well-recognized problem impacting almost all fields of scientific study …

Data analytics is the process of analyzing raw data to make predictions and derive
conclusions. This process involves collecting and organizing data to discover hidden …

Data-oriented applications, their users, and even the law require data of high quality.
Research has broken down the rather vague notion of data quality into various dimensions …

Nowadays, machine learning (ML) plays a vital role in many aspects of our daily life. In
essence, building well-performing ML applications requires the provision of high-quality …

Data cleaning is a crucial yet challenging task in data analysis, often requiring significant
manual effort. To automate data cleaning, previous systems have relied on statistical rules …

Patterns (or regex-based expressions) are widely used to constrain the format of a domain
(or a column), eg, a Year column should contain only four digits, and thus a value like “1980 …

Machine learning algorithms have become increasingly prevalent in multiple domains, such
as autonomous driving, healthcare, and finance. In such domains, data preparation remains …

The problem of comparing database instances with incompleteness is prevalent in
applications such as analyzing how a dataset has evolved over time (eg, data versioning) …

This paper addresses a regression problem in which output label values are the results of
sensing the magnitude of a phenomenon. A low value of such labels can mean either that …

Real-world datasets often suffer from various data quality problems. Several data cleaning
solutions have been proposed so far. However, data cleaning remains a manual and …