Profiling data to determine metadata about a given dataset is an important and frequent
activity of any IT professional and researcher and is necessary for various use-cases. It …

is to understand the dataset at hand and its metadata. The process of metadata discovery is
known as data profiling. Profiling activities range from ad-hoc approaches, such as eye …

Data profiling comprises a broad range of methods to efficiently analyze a given data set. In
a typical scenario, which mirrors the capabilities of commercial data profiling tools, tables of …

The discovery of all unique (and non-unique) column combinations in a given dataset is at
the core of any data profiling effort. The results are useful for a large number of areas of data …

Inclusion dependencies (INDs) are a well-known type of data dependency, specifying that
the values of one column are contained in those of another column. INDs can be used for …

Determining if two sets are related-that is, if they have similar values or if one set contains
the other-is an important problem with many applications in data cleaning, data integration …

Matching dependencies (MDs) have recently been proposed to make data dependencies
tolerant to various information representations, and found useful in data quality applications …

This paper introduces a class of conditional inclusion dependencies (CINDs), which extends
inclusion dependencies (INDs) by enforcing patterns of semantically related data values. We …

Inclusion dependencies form one of the most fundamental classes of integrity constraints.
Their importance in classical data management is reinforced by modern applications such …

MSC2020: 68P15 Received July 7, 2023 Research article A er revision August 1, 2023 Full
text in Russian Accepted August 2, 2023 e paper discusses a new type of dependency in …