Entity information life cycle for big data : master data management and information integration /
Talburt, John R.
Entity information life cycle for big data : master data management and information integration / John R. Talburt, Yinle Zhou - Waltham, MA : Elsevier/Morgan Kaufmann, 2015. - xviii, 235 pages : illustrations ; 24 cm
Includes bibliographical references and index
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
9780128005378
Big data
Semantic Web
Pattern recognition systems
Data mining
CIR HD 30.215 / T35 2015
Entity information life cycle for big data : master data management and information integration / John R. Talburt, Yinle Zhou - Waltham, MA : Elsevier/Morgan Kaufmann, 2015. - xviii, 235 pages : illustrations ; 24 cm
Includes bibliographical references and index
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
9780128005378
Big data
Semantic Web
Pattern recognition systems
Data mining
CIR HD 30.215 / T35 2015