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.
- First book on a topic of growing importance for applications.
- Brings together research from various areas like databases,
statistics, information retrieval, data mining, and machine
- Details the data matching process step by step.
- Includes an overview of freely available data matching systems
and a detailed discussion of practical aspects and limitations.
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.
- 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.
- 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.