Functional and inclusion dependency discovery is important to knowledge discovery,
database semantics analysis, database design, and data quality assessment. Motivated by …

This is an overview of the end-to-end data cleaning process. Data quality is one of the most
important problems in data management, since dirty data often leads to inaccurate data …

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 …

Data cleaning is an important problem and data quality rules are the most promising way to
face it with a declarative approach. Previous work has focused on specific formalisms, such …

Data quality is one of the most important problems in data management. A database system
typically aims to support the creation, maintenance and use of large amount of data …

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

Big data is typically characterized with 4V's: Volume, Velocity, Variety and Veracity. When it
comes to big graphs, these challenges become even more staggering. Each and every of …

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

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 …

Recently, there has been a renovated interest in functional dependencies due to the
possibility of employing them in several advanced database operations, such as data …