Data analysis often uses data sets that were collected for different purposes. Indeed, new
insights are often obtained by combining data sets that were produced independently of …

Traditional error correction solutions leverage handmaid rules or master data to find the
correct values. Both are often amiss in real-world scenarios. Therefore, it is desirable to …

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 integration is the problem of consolidating information provided by multiple sources.
After schema mapping and duplicate detection have been dealt with, the problem consists in …

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) …

The detection of constraint-based errors is a critical task in many data cleaning solutions.
Previous works perform the task either using traditional data management systems or using …

We investigate the problem of discovering approximate denial constraints (DCs), for finding
DCs that hold with some exceptions to avoid overfitting real-life dirty data and facilitate data …

Denial constraints (DCs) are an integrity constraint formalism widely used to detect
inconsistencies in data. Several algorithms have been devised to discover DCs from data …

Data quality is a problem studied in many different research disciplines like computer
science, statistics and economics. More often than not, these different disciplines come with …

In this paper, we propose and study a type of tuple-level constraint that arises from the
selection operator of relational algebra and that closely resembles the concepts of tuple …