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.
