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Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection (Data-Centric Systems and Application
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Data matching (also known as record or data linkage, entity resolution, object identification, or field matching) is the task of identifying, matching and merging records that correspond to the same entities from several databases or even within one database. 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.
Peter Christen’s book is divided into three parts: Part I, “Overview”, introduces the subject by presenting several sample applications and their special challenges, as well as a general overview of a generic data matching process. Part II, “Steps of the Data Matching Process”, then details its main steps like pre-processing, indexing, field and record comparison, classification, and quality evaluation. Lastly, part III, “Further Topics”, deals with specific aspects like privacy, real-time matching, or matching unstructured data. Finally, it briefly describes the main features of many research and open source systems available today.
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.- Sales Rank: #758755 in eBooks
- Published on: 2012-07-04
- Released on: 2012-07-04
- Format: Kindle eBook
Review
"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
From the Back Cover
Data matching (also known as record or data linkage, entity resolution, object identification, or field matching) is the task of identifying, matching and merging records that correspond to the same entities from several databases or even within one database. 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.
Peter Christen’s book is divided into three parts: Part I, “Overview”, introduces the subject by presenting several sample applications and their special challenges, as well as a general overview of a generic data matching process. Part II, “Steps of the Data Matching Process”, then details its main steps like pre-processing, indexing, field and record comparison, classification, and quality evaluation. Lastly, part III, “Further Topics”, deals with specific aspects like privacy, real-time matching, or matching unstructured data. Finally, it briefly describes the main features of many research and open source systems available today.
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.
About the Author
Peter Christen is Senior Lecturer at the Research School of Computer Science at the Australian National University in Canberra, Australia. His research interests are data mining, with a focus on data matching, and privacy-preserving data sharing and mining. He has published over 50 papers in these areas, and he is the principle developer of the `Febrl' (Freely Extensible Biomedical Record Linkage) open source data cleaning, deduplication and record linkage system.
Most helpful customer reviews
2 of 2 people found the following review helpful.
An excellent book on an important topic
By Terence Mills
I am a statistician working in a hospital. Part of my work involves data matching at a very elementary level, but I am no expert! Still, I can see that this is an important topic. The amount of data collected in health care is huge: the challenge is to match data from different sources rather than collect more data. By matching data from different sources we can gain a more complete picture of how people travel through the health care system. You can imagine the difficulties when my name is recorded as "Terence" here, "Terrance" there, "Terry" somewhere else, and, of course, "Terri" (??). Peter Christen is a computer scientist who is an expert in this field. Furthermore, he writes well. So here we have a book that is well-written, by an expert, on an important topic, published by a major publisher, at a reasonable price. Although the book is probably more advanced than I need, suprisingly, I do not regard it as too technical. There is an extensive list of references. With only about 250 pages, I can read it on the train. For a hardback book it is a good price - many university text books cost much more. "Data Matching" by Peter Christen should be in every university library.
3 of 4 people found the following review helpful.
Comprehensive
By Harrisjh
This book is a must for anyone in the field of data linkage (or record linkage as it is also known as).
It provides a comprehensive guide to all the steps in the process with clear examples.
There's not too much around on this topic, so this text is much needed.
2 of 2 people found the following review helpful.
Fantastic!
By Cheng
This is a fantastic book for data matching. It is written very well and easy to be understood. It strength my foundation of data matching.
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