Detecting and repairing dirty data is one of the perennial challenges in data analytics, and
failure to do so can result in inaccurate analytics and unreliable decisions. Over the past few …

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 …

Integrity constraints are semantic conditions that a database should satisfy in order to be an
appropriate model of external reality. In practice, and for many reasons, a database may not …

Data quality is one of the most important problems in data management, since dirty data
often leads to inaccurate data analytics results and wrong business decisions. Poor data …

One of the main challenges that data cleaning systems face is to automatically identify and
repair data errors in a dependable manner. Though data dependencies (aka integrity …

Central to a data cleaning system are record matching and data repairing. Matching aims to
identify tuples that refer to the same real-world object, and repairing is to make a database …

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OPQRSTU@'VWIABXYZ [\],@ AB'KLVW^ _I!" AB'aZbc deABQ!^ fS ABXYZghiKjk l# $8 J …

Entity resolution is a fundamental problem in data integration dealing with the combination
of data from different sources to a unified view of the data. Entity resolution is inherently an …

Data quality is one of the most important problems in data management, since dirty data
often leads to inaccurate data analytics results and wrong business decisions. Data cleaning …

In this article we review the main concepts around database repairs and consistent query
answering, with emphasis on tracing back the origin, motivation, and early developments …