One of the most critical tasks for improving data quality and increasing the reliability of data
analytics is Entity Resolution (ER), which aims to identify different descriptions that refer to …

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 …

This book is the result of a study path that started in 2006, when the two authors of this book
published the book Data Quality: Concepts, Methodologies and Techniques. After 8 years …

Often, in the real world, entities have two or more representations in databases. Duplicate
records do not share a common key and/or they contain errors that make duplicate matching …

The automatic extraction of information from unstructured sources has opened up new
avenues for querying, organizing, and analyzing data by drawing upon the clean semantics …

Many datasets of interest today are best described as a linked collection of interrelated
objects. These may represent homogeneous networks, in which there is a single-object type …

Written for practitioners of data mining, data cleaning and database management. Presents
a technical treatment of data quality including process, metrics, tools and algorithms …

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 …

Despite the huge amount of recent research efforts on entity resolution (matching) there has
not yet been a comparative evaluation on the relative effectiveness and efficiency of …