Entity Information Life Cycle for Big Data: Master Data Management and Information Integration

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9780128005378: Entity Information Life Cycle for Big Data: Master Data Management and Information Integration

Entity Information Life Cycle for Big Data walks you through the ins and outs of managing entity information so you can successfully achieve master data management (MDM) in the era of big data. This book explains big data’s impact on MDM and the critical role of entity information management system (EIMS) in successful MDM. Expert authors Dr. John R. Talburt and Dr. Yinle Zhou provide a thorough background in the principles of managing the entity information life cycle and provide practical tips and techniques for implementing an EIMS, strategies for exploiting distributed processing to handle big data for EIMS, and examples from real applications. Additional material on the theory of EIIM and methods for assessing and evaluating EIMS performance also make this book appropriate for use as a textbook in courses on entity and identity management, data management, customer relationship management (CRM), and related topics.

  • Explains the business value and impact of entity information management system (EIMS) and directly addresses the problem of EIMS design and operation, a critical issue organizations face when implementing MDM systems
  • Offers practical guidance to help you design and build an EIM system that will successfully handle big data
  • Details how to measure and evaluate entity integrity in MDM systems and explains the principles and processes that comprise EIM
  • Provides an understanding of features and functions an EIM system should have that will assist in evaluating commercial EIM systems
  • Includes chapter review questions, exercises, tips, and free downloads of demonstrations that use the OYSTER open source EIM system
  • Executable code (Java .jar files), control scripts, and synthetic input data illustrate various aspects of CSRUD life cycle such as identity capture, identity update, and assertions

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About the Author:

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.

Dr. Yinle Zhou is an IBM software architect and data scientist in the InfoSphere MDM development group in Austin, Texas, and also serves as an Affiliate Member of the Graduate Faculty at University of Arkansas at Little Rock (UALR). Dr. Zhou holds a PhD in Integrated Computing with Emphasis in Information Quality (IQ) from UALR where her doctoral research focused on modeling the management of entity identity information in entity resolution systems. She also holds a Master of Science in Information Quality from UALR, a Bachelor of Business Administration in Electronic Commerce from Nanjing University in China, and the Information Quality Certified Professional (IQCP) credential issued by the International Association for Information and Data Quality (IAIDQ). Her research and publications are in areas of information quality, identity management, entity and identity resolution, and social computing

Review:

"...good as a textbook and also as independent reading for professionals in the sector. The references where the solutions and methods treated were originally presented are included so that they can be found for further reading...a highly recommended book." --Computing Reviews, Entity Information Life Cycle for Big Data

"... covers MDM in a traditional manner, that is, maintaining current entity identity information, very well, and it can be recommended to advanced MDM practitioners (and advanced students of databases and data warehouses)." --Computing Reviews

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Book Description ELSEVIER SCIENCE TECHNOLOGY, United States, 2015. Paperback. Book Condition: New. 234 x 190 mm. Language: English . Brand New Book ***** Print on Demand *****. Entity Information Life Cycle for Big Data walks you through the ins and outs of managing entity information so you can successfully achieve master data management (MDM) in the era of big data. This book explains big data s impact on MDM and the critical role of entity information management system (EIMS) in successful MDM. Expert authors Dr. John R. Talburt and Dr. Yinle Zhou provide a thorough background in the principles of managing the entity information life cycle and provide practical tips and techniques for implementing an EIMS, strategies for exploiting distributed processing to handle big data for EIMS, and examples from real applications. Additional material on the theory of EIIM and methods for assessing and evaluating EIMS performance also make this book appropriate for use as a textbook in courses on entity and identity management, data management, customer relationship management (CRM), and related topics. Bookseller Inventory # AAV9780128005378

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Book Description ELSEVIER SCIENCE TECHNOLOGY, United States, 2015. Paperback. Book Condition: New. 234 x 190 mm. Language: English . Brand New Book ***** Print on Demand *****.Entity Information Life Cycle for Big Data walks you through the ins and outs of managing entity information so you can successfully achieve master data management (MDM) in the era of big data. This book explains big data s impact on MDM and the critical role of entity information management system (EIMS) in successful MDM. Expert authors Dr. John R. Talburt and Dr. Yinle Zhou provide a thorough background in the principles of managing the entity information life cycle and provide practical tips and techniques for implementing an EIMS, strategies for exploiting distributed processing to handle big data for EIMS, and examples from real applications. Additional material on the theory of EIIM and methods for assessing and evaluating EIMS performance also make this book appropriate for use as a textbook in courses on entity and identity management, data management, customer relationship management (CRM), and related topics. Bookseller Inventory # AAV9780128005378

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