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Entity Resolution and Information Quality
 
 

Entity Resolution and Information Quality, 1st Edition

 
Entity Resolution and Information Quality, 1st Edition,John R. Talburt,ISBN9780123819727
 
 
 

  

Morgan Kaufmann

9780123819727

9780123819734

256

235 X 191

Learn how to integrate and use your customer and product information data to stay ahead of your competition!

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Key Features

  • First authoritative reference explaining entity resolution and how to use it effectively
  • Provides practical system design advice to help you get a competitive advantage
  • Includes a companion site with synthetic customer data for applicatory exercises, and access to a Java-based Entity Resolution program.

Description

Customers and products are the heart of any business, and corporations collect more data about them every year. However, just because you have data doesn’t mean you can use it effectively. If not properly integrated, data can actually encourage false conclusions that result in bad decisions and lost opportunities. Entity Resolution (ER) is a powerful tool for transforming data into accurate, value-added information. Using entity resolution methods and techniques, you can identify equivalent records from multiple sources corresponding to the same real-world person, place, or thing.

This emerging area of data management is clearly explained throughout the book. It teaches you the process of locating and linking information about the same entity - eliminating duplications - and making crucial business decisions based on the results. This book is an authoritative, vendor-independent technical reference for researchers, graduate students and practitioners, including architects, technical analysts, and solution developers. In short, Entity Resolution and Information Quality gives you the applied level know-how you need to aggregate data from disparate sources and form accurate customer and product profiles that support effective marketing and sales. It is an invaluable guide for succeeding in today’s info-centric environment.

Readership

Database administrators, data/Information analysts, information and enterprise architects, data warehouse and systems engineers, and software developers working on an identity resolution engine or middleware stack.

John R. Talburt

Dr. John R. Talburt is Professor of Information Science at the University of Arkansas at Little Rock (UALR) where he is the Coordinator for the Information Quality Graduate Program and the Executive Director of the UALR Center for Advanced Research in Entity Resolution and Information Quality (ERIQ). He is also the Chief Scientist for Black Oak Partners, LLC, an information quality solutions company. Prior to his appointment at UALR he was the leader for research and development and product innovation at Acxiom Corporation, a global leader in information management and customer data integration. Professor Talburt holds several patents related to customer data integration and the author of numerous articles on information quality and entity resolution, and is the author of Entity Resolution and Information Quality (Morgan Kaufmann, 2011). He also holds the IAIDQ Information Quality Certified Professional (IQCP) credential.

Affiliations and Expertise

Professor of Information Science, University of Arkansas at Little Rock; Executive Director of the UALR Laboratory for Advanced Research in Entity Resolution and Information Quality; Associate Director of the Acxiom Laboratory for Applied Research; Co-Director of the MIT Information Quality Program’s Working Group on Customer-Centric Information Quality Management.

Entity Resolution and Information Quality, 1st Edition

Foreword

Preface

Acknowledgements

Chapter 1 Principles of Entity Resolution

Entity Resolution

Entity Resolution Activities

Summary

Review Questions

Chapter 2 Principles of Information Quality

Information Quality

IQ and the Quality of Information

Two IP Examples

IQ Management

Information versus Process

IQ  and  HPC

The Evolution of Information Quality

IQ as an Academic Discipline

IQ  and  ER

Summary

Review Questions

Chapter 3 Entity Resolution Models

Overview

The Fellegi-Sunter Model

SERF Model

Algebraic Model

ENRES Meta-Model

Summary

Review Questions

Chapter 4 Entity-Based Data Integration

Introduction

Formal Framework for Describing EBDI

viiOptimizing Selection Operator Accuracy

More Complex Selection Rules

Summary

Review Questions

Chapter 5 Entity Resolution Systems

Introduction

DataFlux dfPowerStudio

Infoglide Identity Resolution Engine

Acxiom AbiliTec

Summary

Review Questions

Chapter 6 The OYSTER Project

Background

OYSTER Logic

Transitive Equivalence Example

Asserted Equivalence Example

Febrl: Open-Source Project

Summary

Review Questions

Chapter 7 Trends in Entity Resolution Research and Applications

Introduction

ER and Information Hubs

Association Analysis and Social Networks

HPC  in  ER

Integration of ER and IQ

Entity-Based Data Integration

Fundamental ER Research

Summary

Review Questions

Bibliography

Glossary

Appendix

Index

Quotes and reviews

"This book is comprehensive, timely, and on the leading edge of the topic. In addition to being comprehensive and systematic, the book has two distinct characteristics: (1) it addresses the issue of entity relationships, which go beyond entity matching. This novel approach generates much richer information about entities; (2) it discusses not only techniques, but also systems that implement the techniques. This system-oriented approach helps the reader to see how to apply the techniques for problem solving."--Dr. Hongwei (Harry) Zhu - Assistant Professor of Information Technology in the College of Business and Public Administration, Old Dominion University

"Talburt, the author of this book, is one of the organizers of the first graduate degree program in information quality, hosted by the University of Arkansas at Little Rock. The book contains seven easy-to-read chapters. A chapter on trends and research topics in entity resolution closes this short textbook. Some of the suggestions will undoubtedly encourage graduate students to pursue their research on data integration topics. The book offers interesting pointers and bibliographic references for exploring new avenues of research."--Computing Reviews

"Talburt (information science, U. of Arkansas-Little Rock) presents a textbook developed from a graduate course on the two emerging specialties within information science. Students tend to come from a number of disciplines, so no deep background in information science is assumed, and the material may even be suitable for upper-level undergraduate courses. He covers principles of entity resolution and information quality, entity resolution models and systems, entity-based data integration, the OYSTER open-source software development project, and trends in research and applications."--SciTech Book News

 
 
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