Entity Resolution and Information QualityBy
- John R. Talburt, 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.
Customers and products are the heart of any business, and corporations collect more data about them every year. However, just because you have data doesnt 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 todays info-centric environment.
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.
Paperback, 256 Pages
Published: December 2010
Imprint: Morgan Kaufmann
"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
AcknowledgementsChapter 1 Principles of Entity Resolution
Entity ResolutionEntity Resolution Activities
Chapter 2 Principles of Information QualityInformation Quality
IQ and the Quality of InformationTwo IP Examples
IQ ManagementInformation versus Process
IQ and HPCThe Evolution of Information Quality
IQ as an Academic DisciplineIQ and ER
Chapter 3 Entity Resolution ModelsOverview
The Fellegi-Sunter ModelSERF Model
Algebraic ModelENRES Meta-Model
Chapter 4 Entity-Based Data IntegrationIntroduction
Formal Framework for Describing EBDIviiOptimizing Selection Operator Accuracy
More Complex Selection RulesSummary
Review QuestionsChapter 5 Entity Resolution Systems
Infoglide Identity Resolution EngineAcxiom AbiliTec
Chapter 6 The OYSTER ProjectBackground
OYSTER LogicTransitive Equivalence Example
Asserted Equivalence ExampleFebrl: Open-Source Project
Chapter 7 Trends in Entity Resolution Research and ApplicationsIntroduction
ER and Information HubsAssociation Analysis and Social Networks
HPC in ERIntegration of ER and IQ
Entity-Based Data IntegrationFundamental ER Research