Data Matching

1. Front Matter

   Pages i-xix

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2. Overview

   1. Front Matter

      Pages 1-1

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   2. Introduction

      • Peter Christen

      Pages 3-22

   3. The Data Matching Process

      • Peter Christen

      Pages 23-35

3. Steps of the Data Matching Process

   1. Front Matter

      Pages 37-37

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4. Steps of the DataMatching Process

   1. Data Pre-Processing

      • Peter Christen

      Pages 39-67

   2. Indexing

      • Peter Christen

      Pages 69-100

   3. Field and Record Comparison

      • Peter Christen

      Pages 101-127

   4. Classification

      • Peter Christen

      Pages 129-162

   5. Evaluation of Matching Quality and Complexity

      • Peter Christen

      Pages 163-184

5. Further Topics

   1. Front Matter

      Pages 185-185

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   2. Privacy Aspects of Data Matching

      • Peter Christen

      Pages 187-207

   3. Further Topics and Research Directions

      • Peter Christen

      Pages 209-228

   4. Data Matching Systems

      • Peter Christen

      Pages 229-242

6. Back Matter

   Pages 243-270

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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.

• data consistency
• data management
• data matching
• data quality
• duplicate detection
• entity resolution
• field matching
• record linkage

"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

• School of Computer Science, The Australian National University, Canberra, Australia

  Peter Christen

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.