Erschienen: 29.06.2015 Abbildung von Duan / Chakrabarty / Zeng | Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System | 2015 | 2015

Duan / Chakrabarty / Zeng

Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System

lieferbar ca. 10 Tage als Sonderdruck ohne Rückgaberecht

117,69 €

inkl. Mwst.

auch verfügbar als eBook (PDF) für 89.24 €

2015 2015. Buch. xii, 160 S. 29 s/w-Abbildungen, 47 Farbabbildungen, 15 s/w-Tabelle, Bibliographien. Hardcover

Springer. ISBN 978-3-319-18737-2

Format (B x L): 15,5 x 23,5 cm

Gewicht: 426 g

In englischer Sprache


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.   ·         Addresses system complexity by studying the information system as a mass-customization enterprise; ·         Provides practical engineering solutions for real-time applications and data-driven prediction; ·         Uses real data and an industry-strength simulation platform that mimics the features of a real enterprise; ·         Offers a technology-synthesis platform, combining different techniques such as simulation, optimization, statistical methods and machine-learning algorithms.      


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