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Synopsis
This book provides a comprehensive set of optimization and prediction techniques for an enterprise information system. Readers with a background in operations research, system engineering, statistics, or data analytics can use this book as a reference to derive insight from data and use this knowledge as guidance for production management. The authors identify the key challenges in enterprise information management and present results that have emerged from leading-edge research in this domain. Coverage includes topics ranging from task scheduling and resource allocation, to workflow optimization, process time and status prediction, order admission policies optimization, and enterprise service-level performance analysis and prediction. With its emphasis on the above topics, this book provides an in-depth look at enterprise information management solutions that are needed for greater automation and reconfigurability-based fault tolerance, as well as to obtain data-driven recommendations for effective decision-making.
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About the Author
Qing Duan is a data scientist at Paypal, Inc. Krishnendu Chakrabarty is a Professor in the Department of Electrical and Computer Engineering at Duke University. Jun Zeng is a principal researcher at Hewlett-Packard Labs.
From the Back Cover
This book provides a comprehensive set of optimization and prediction techniques for an enterprise information system. Readers with a background in operations research, system engineering, statistics, or data analytics can use this book as a reference to derive insight from data and use this knowledge as guidance for production management. The authors identify the key challenges in enterprise information management and present results that have emerged from leading-edge research in this domain. Coverage includes topics ranging from task scheduling and resource allocation, to workflow optimization, process time and status prediction, order admission policies optimization, and enterprise service-level performance analysis and prediction. With its emphasis on the above topics, this book provides an in-depth look at enterprise information management solutions that are needed for greater automation and reconfigurability-based fault tolerance, as well as to obtain data-driven recommendations for effective decision-making.
"About this title" may belong to another edition of this title.
- PublisherSpringer
- Publication date2015
- ISBN 10 3319187376
- ISBN 13 9783319187372
- BindingHardcover
- LanguageEnglish
- Number of pages172
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Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System
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ISBN 10: 3319187376
ISBN 13: 9783319187372
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Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Addresses system complexity by studying the information system as a mass-customization enterpriseProvides practical engineering solutions for real-time applications and data-driven predictionUses real data and an industry-strength simulatio. Seller Inventory # 33838405
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Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a comprehensive set of optimization and prediction techniques for an enterprise information system. Readers with a background in operations research, system engineering, statistics, or data analytics can use this book as a reference to derive insight from data and use this knowledge as guidance for production management. The authors identify the key challenges in enterprise information management and present results that have emerged from leading-edge research in this domain. Coverage includes topics ranging from task scheduling and resource allocation, to workflow optimization, process time and status prediction, order admission policies optimization, and enterprise service-level performance analysis and prediction. With its emphasis on the above topics, this book provides an in-depth look at enterprise information management solutions that are needed for greater automation and reconfigurabilty-based fault tolerance, as well as to obtain data-driven recommendations for effective decision-making. 172 pp. Englisch. Seller Inventory # 9783319187372
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Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System
Published by
Springer International Publishing, 2015
ISBN 10: 3319187376
ISBN 13: 9783319187372
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a comprehensive set of optimization and prediction techniques for an enterprise information system. Readers with a background in operations research, system engineering, statistics, or data analytics can use this book as a reference to derive insight from data and use this knowledge as guidance for production management. The authors identify the key challenges in enterprise information management and present results that have emerged from leading-edge research in this domain. Coverage includes topics ranging from task scheduling and resource allocation, to workflow optimization, process time and status prediction, order admission policies optimization, and enterprise service-level performance analysis and prediction. With its emphasis on the above topics, this book provides an in-depth look at enterprise information management solutions that are needed for greater automation and reconfigurabilty-based fault tolerance, as well as to obtain data-driven recommendations for effective decision-making. Seller Inventory # 9783319187372
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Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System
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Buch. Condition: Neu. Neuware -This book provides a comprehensive set of optimization and prediction techniques for an enterprise information system. Readers with a background in operations research, system engineering, statistics, or data analytics can use this book as a reference to derive insight from data and use this knowledge as guidance for production management. The authors identify the key challenges in enterprise information management and present results that have emerged from leading-edge research in this domain. Coverage includes topics ranging from task scheduling and resource allocation, to workflow optimization, process time and status prediction, order admission policies optimization, and enterprise service-level performance analysis and prediction. With its emphasis on the above topics, this book provides an in-depth look at enterprise information management solutions that are needed for greater automation and reconfigurability-based fault tolerance, as well as to obtain data-driven recommendations for effective decision-making.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 172 pp. Englisch. Seller Inventory # 9783319187372
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Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System
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