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

  • Book
  • © 2015

Overview

  • 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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Table of contents (7 chapters)

Keywords

About this book

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.

Authors and Affiliations

  • PayPal, Inc., San Jose, USA

    Qing Duan

  • ECE, Duke University, Durham, USA

    Krishnendu Chakrabarty

  • Hewlett-Packard Labs, Palo Alto, USA

    Jun Zeng

About the authors

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.

Bibliographic Information

  • Book Title: Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System

  • Authors: Qing Duan, Krishnendu Chakrabarty, Jun Zeng

  • DOI: https://doi.org/10.1007/978-3-319-18738-9

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer International Publishing Switzerland 2015

  • Hardcover ISBN: 978-3-319-18737-2Published: 29 June 2015

  • Softcover ISBN: 978-3-319-36429-2Published: 15 October 2016

  • eBook ISBN: 978-3-319-18738-9Published: 13 June 2015

  • Edition Number: 1

  • Number of Pages: XII, 160

  • Number of Illustrations: 29 b/w illustrations, 47 illustrations in colour

  • Topics: Communications Engineering, Networks, Circuits and Systems, Information Storage and Retrieval

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