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 …

In our Big Data era, data is being generated, collected and analyzed at an unprecedented
scale, and data-driven decision making is swee** through all aspects of society. Recent …

We propose a class of integrity constraints for relational databases, referred to as conditional
functional dependencies (CFDs), and study their applications in data cleaning. In contrast to …

This paper investigates the discovery of conditional functional dependencies (CFDs). CFDs
are a recent extension of functional dependencies (FDs) by supporting patterns of …

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 …

Data profiling refers to the activity of collecting data about data,{ie}, metadata. Most IT
professionals and researchers who work with data have engaged in data profiling, at least …

In this paper we present GDR, a Guided Data Repair framework that incorporates user
feedback in the cleaning process to enhance and accelerate existing automatic repair …

Dirty data is a serious problem for businesses leading to incorrect decision making,
inefficient daily operations, and ultimately wasting both time and money. Dirty data often …

Dependency theory is almost as old as relational databases themselves, and has
traditionally been used to improve the quality of schema, among other things. Recently there …