1.

Record Nr.

UNINA9910299841103321

Autore

Duan Qing

Titolo

Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System [[electronic resource] /] / by Qing Duan, Krishnendu Chakrabarty, Jun Zeng

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015

ISBN

3-319-18738-4

Edizione

[1st ed. 2015.]

Descrizione fisica

1 online resource (165 p.)

Disciplina

025.04

620

621.3815

621.382

Soggetti

Electrical engineering

Electronic circuits

Information storage and retrieval

Communications Engineering, Networks

Circuits and Systems

Information Storage and Retrieval

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references.

Nota di contenuto

Introduction -- Production Simulation Platform -- Production Workflow Optimizations -- Predictions of Process-Execution Time and Process-Execution Status -- Optimization of Order-Admission Policies -- Conclusion.

Sommario/riassunto

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.