A knowledge integration framework for complex network management

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.