Data Matching - Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection


Peter Christen

Springer Data-Centric Systems and Applications

Hardcover, August 2012
274 pages, 66 illustrations.

ISBN 978-3-642-31163-5

Preface, table of contents, and references are available for download.

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The book is very well organized and exceptionally well written. Because of the depth, amount, and quality of the material that is covered, I would expect this book to be one of the standard references in future years. - William E. Winkler, U.S. Bureau of the Census, Washington, DC, USA.

About this book

Data matching is the task of identifying, matching and merging records that correspond to the same entities from several databases or even within one database. This process is also known as record linkage, data linkage, entity resolution, name disambiguation, author disambiguation, object identification, co-reference resolution, object identification, data reconciliation, citation matching, reference matching, field matching, identity uncertainty, duplicate detection, deduplication, authority control, approximate string join, similarity search, merge/purge, list washing, data cleansing, or field scrubbing.

Based on research in various domains including applied statistics, health informatics, data mining, machine learning, artificial intelligence, database management, and digital libraries, significant advances have been achieved over the last decade in all aspects of the data matching process, especially on how to improve the accuracy of data matching, and its scalability to large databases.

The book is divided into three parts:

By providing the reader with a broad range of data matching concepts and techniques and touching on all aspects of the data matching process, this book helps researchers as well as students specializing in data quality or data matching aspects to familiarize themselves with recent research advances and to identify open research challenges in the area of data matching. To this end, each chapter of the book includes a final section that provides pointers to further background and research material. Practitioners will better understand the current state of the art in data matching as well as the internal workings and limitations of current systems. Especially, they will learn that it is often not feasible to simply implement an existing off-the-shelf data matching system without substantial adaption and customization. Such practical considerations are discussed for each of the major steps in the data matching process.