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 …

Functional dependencies are important metadata used for schema normalization, data
cleansing and many other tasks. The efficient discovery of functional dependencies in tables …

Existing techniques for unionable table search define unionability using metadata (tables
must have the same or similar schemas) or column-based metrics (for example, the values …

This paper investigates how to adapt standard classification rule learning approaches to
subgroup discovery. The goal of subgroup discovery is to find rules describing subsets of the …

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

Numerous measures are used for performance evaluation in machine learning. In predictive
knowledge discovery, the most frequently used measure is classification accuracy. With new …

Functional dependencies are structural metadata that can be used for schema
normalization, data integration, data cleansing, and many other data management tasks …

Cardinality estimation is a fundamental and critical problem in databases. Recently, many
estimators based on deep learning have been proposed to solve this problem and they have …