Entity Information Life Cycle for Big Data
Language: English
Published by Elsevier Inc, 2015
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Condition: New
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Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - 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.
Seller Inventory # 9780128005378
- Title
- Entity Information Life Cycle for Big Data
- Author
- Yinle Zhou
- Publisher
- Elsevier Inc
- Publication year
- 2015
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 0128005378
- ISBN 13
- 9780128005378
- Item weight
- 524 grams
- Dimensions
- 231x184x20 mm
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.
"Synopsis" may belong to another edition of this title.
About the Author
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
"About the title" may belong to another edition of this title.
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