Name: Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System (en Inglés)
SKU: 57635846
Price: 96.42 USD
Availability: InStock
Author: Duan, Qing ; Chakrabarty, Krishnendu ; Zeng, Jun
ISBN: 9783319364292

portada Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System (en Inglés)

Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System (en Inglés)

Formato

Libro Físico

Autor

Krishnendu Chakrabarty

Qing Duan

Jun Zeng

Editorial

Springer

Idioma

Inglés

N° páginas

160

Encuadernación

Tapa Blanda

ISBN13

9783319364292

Categorías

Electrónica: circuitos y componentes
Ingeniería de las comunicaciones/las telecomunicaciones
Medios de almacenamiento y dispositivos periféricos
Recuperación de información
Administración del sistema

Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System (en Inglés)

Krishnendu Chakrabarty (Autor) · Qing Duan (Autor) · Jun Zeng (Autor) · Springer · Tapa Blanda

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Reseña del libro "Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System (en Inglés)"

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.