A knowledge integration framework for complex network management
| Published date | 02 October 2007 |
| DOI | https://doi.org/10.1108/02635570710822769 |
| Pages | 1089-1109 |
| Date | 02 October 2007 |
| Author | Xiangyang Li,Charu Chandra |
| Subject Matter | Economics,Information & knowledge management,Management science & operations |
A knowledge integration
framework for complex network
management
Xiangyang Li and Charu Chandra
Department of Industrial and Manufacturing Systems Engineering,
University of Michigan-Dearborn, Dearborn, Michigan, USA
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 Information systems,Supply chain management, Riskmanagement, Computer networks
Paper type Research paper
Introduction
Evolving into complex networks, critical national infrastructures play essential roles in
supporting the modern society and the globalized economy. These important facilities
include computer networks, supply chain/network, power grid, financial networks,
ad hoc wireless networks, sensor networks, disaster surveillance and response
systems, transportation infrastructures, healthcare systems, intelligence systems,
social networks, among others. Great demand exists to strengthen the quality and
reliability of these infrastructures critical to national interests, in terms of various
performance metri cs. With new theories , latest computing te chnologies, and
ever-growing computational capacities, novel solutions to describe such networks,
understand the problems, and optimize their performance are constantly emerging.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0263-5577.htm
Knowledge
integration
framework
1089
Industrial Management & Data
Systems
Vol. 107 No. 8, 2007
pp. 1089-1109
qEmerald Group Publishing Limited
0263-5577
DOI 10.1108/02635570710822769
Recent developments in wired and wireless technology have greatly expedited
the evolution of large networks and enhanced the information and knowledge
processing in these systems (Varshney et al., 2000). New sensor and storage
technologies such as RFID tagging technique (Brewer et al., 1999) and ad hoc
wireless networks largely reduce the information monitoring and collection time,
and allow the central management module to gain live information updated
continuously about virtually every component (Jin et al., 2004). Computer systems
and infrastructures themselves have become increasingly complex in the form of
large-scale, multi-application, heterogeneous platforms, and special requirements on
service type and quality. A computer network infrastructure as a whole has to
manage and defend itself actively and confidently against malicious attacks, in a
timely and accurate manner, just like a military force in a dangerous battlefield. In
another example, imagine a large supply chain network that consists of thousands
of business nodes. No matter whether they are located at headquarter, cen ter,
branch, department, or even individual employee and facility, these nodes are
linked flexibly. Ideally, they can at any instant exchange with each other, business
knowledge about inventory, capacity, quality, prediction, failure, etc. and are
impacted by management strategies of such knowledge. Such a system can extend
across geographical boundaries and run uninterruptedly, synchronized by dawn
and sunset across continents.
These complex networks generate, contain and process business knowledge
continuously. Business and network knowledge is captured in features and variables
that represent the state of various nodes in this network, such as an error message
generated at one network node, or repair orders at a customer service branch. It is not
enough to apply deterministic solutions such as rule-based analysis of logging data in
computer intrusion detection (Axelsson, 2001; Debar et al., 1999), or in traditional
supply chain studies the mathematical models from operations research (Graves et al.,
1998) and game theory (Shubik, 2002). Emerging network structures of modern
networked enterprises raise challenges in terms of complexity and uncertainty. The
information may not be as accurate as required by the above models, as well as that the
limitation of supporting technology such as the communication bandwidth and
computation cost of a wireless network or RFID nodes may not allow accurate and
complete information generated and disseminated as requested, such as the bullwhip
effect in supply chains (Lee et al., 1997; Metters, 1997). All these networks are complex
temporal-spatial systems where the synchronization of data over time (time series data)
and the integration of distributed data (heterogeneous data sources and data
structures) can be an enormous challenge. More significantly, these networks are often
complex adaptive systems that existing models commonly are not able to handle very
well. We have to build up a model that can comprehensively and accurately capture the
knowledge in this complex system, such as in the exploratory study by (Ye et al., 2000;
Choi et al., 2000). Decision making based on heterogeneous and distributed information
is one type of task of knowledge integration or information fusion. We must be able to
efficiently integrate the knowledge represented in this model, which often has a
network structure.
Therefore, the objective of this paper is to investigate and develop a generic
knowledge integration framework that can handle the challenges posed in emerging
complex networks. We also try to examine such a framework in various
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