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A knowledge integration framework for complex network management

Xiangyang Li (Department of Industrial and Manufacturing Systems Engineering, University of Michigan‐Dearborn, Dearborn, Michigan, USA)
Charu Chandra (Department of Industrial and Manufacturing Systems Engineering, University of Michigan‐Dearborn, Dearborn, Michigan, USA)

Industrial Management & Data Systems

ISSN: 0263-5577

Article publication date: 2 October 2007

3043

Abstract

Purpose

Large supply and computer networks contain heterogeneous information and correlation among their components, and are distributed across a large geographical region. This paper aims to investigate and develop a generic knowledge integration framework that can handle the challenges posed in complex network management. It also seeks to examine this framework in various applications of essential management tasks in different infrastructures.

Design/methodology/approach

Efficient information and knowledge integration technologies are key to capably handling complex networks. An adaptive fusion framework is proposed that takes advantage of dependency modelling, active configuration planning and scheduling, and quality assurance of knowledge integration. The paper uses cases of supply network risk management and computer network attack correlation (NAC) to elaborate the problem and describe various applications of this generic framework.

Findings

Information and knowledge integration becomes increasingly important, enabled by technologies to collect and process data dynamically, and faces enormous challenges in handling escalating complexity. Representing these systems into an appropriate network model and integrating the knowledge in the model for decision making, directed by information and complexity measures, provide a promising approach. The preliminary results based on a Bayesian network model support the proposed framework.

Originality/value

First, the paper discussed and defined the challenges and requirements faced by knowledge integration in complex networks. Second, it proposed a knowledge integration framework that systematically models various network structures and adaptively integrates knowledge, based on dependency modelling and information theory. Finally, it used a conceptual Bayesian model to elaborate the application to supply chain risk management and computer NAC of this promising framework.

Keywords

Citation

Li, X. and Chandra, C. (2007), "A knowledge integration framework for complex network management", Industrial Management & Data Systems, Vol. 107 No. 8, pp. 1089-1109. https://doi.org/10.1108/02635570710822769

Publisher

:

Emerald Group Publishing Limited

Copyright © 2007, Emerald Group Publishing Limited

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