An ontology-based similarity measurement for problem-based case reasoning

Author name used in this publication: Adela Lau2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

An Intelligent Methodology for Modeling Semantic Knowledge in Industrial Networks

Networks has been involved in Industrial and IoT Applications for decades, creating new opportunities for more personalized services, improved security, greater automation and operational efficiency. Industry and businesses who prioritize and modernize their analytics strategy and technology to monetize their data will lead and succeed in our data-driven world. The network now provides even more detailed information through units and equipment databases, which provide details about the installed equipment, including models, designed capacity, performance and start / stop dates of the switches, routers, etc. repositories, digital files and business websites. Access to these collections is a serious challenge. Artificial intelligence and the Semantic Web provide a common framework for sharing and reusing knowledge in an efficient way. This article explores the architecture of intelligent agents to make the argument of an intelligent solution as opposed to traditional methods. We propose a new paradigm in which the intelligent management of the network is integrated into the conceptual repository of management information. This study focuses on an intelligent framework and language to formalize knowledge management descriptions and combine them with the existing SNMP management model. Based on the present proposal and the Internet management model, we describe the design and implementation of an integrated intelligent management platform called OntoNetwork

Supporting Case-based Learning in Information Security with Web-based Technology

Case-based learning has been widely used in many disciplines. As an effective pedagogical method, case-based learning is also being used to support teaching and learning in the domain of information security. In this paper, we demonstrate case-based learning in information security by sharing our experiences in using a case study to teach security management. A process model of integrating a case library and Web 2.0 technologies to facilitate case-based learning is also presented in this paper. Insights and recommendations for implementing the process model are offered as well

Collaborative problem solving within supply chains: general framework, process and methodology

The Problem Solving Process is a central element of the firms' continuous improvement strategies. In this framework, a number of approaches have succeeded to demonstrate their effectiveness to tackle industrial problems. The list includes, but is not limited to PDCA, DMAICS, 7Steps and 8D/9S. However, the emergence and increasing emphasis in the supply chains have impacted the effectiveness of those methods to solve problems that go beyond the boundaries of a single firm and, in consequence, their ability to provide solutions when the contexts on which firms operate are distributed. This can be explained because not only the problems, but also the products, partners, skills, resources and pieces of evidence required to solve those problems are distributed, fragmented and decentralized across the network. This PhD thesis deals with the solving of industrial problems in supply chains based in collaboration. It develops a general framework for studying this paradigm, as well as both a generic process and a collaborative methodology able to deal with the process in practice. The proposal considers all the technical aspects (e.g. products modeling and network structure) and the collaborative aspects (e.g. the trust decisions and/or the power gaps between partners) that simultaneously impact the supply chain operation and the jointly solving of problems. Finally, this research work positions the experiential knowledge as a central lever of the problem solving process to contribute to the continuous improvement strategies at a more global level

Résolution collaborative de problèmes au sein des chaînes logistiques : cadre conceptuel, processus et méthodologie

La Résolution de Problèmes est l'un des piliers des stratégies d'amélioration continue des entreprises. Dans ce cadre, un certain nombre des méthodes ont réussi à démontrer son efficacité pour adresser des problèmes particulièrement complexes. Parmi ces méthodes, on peut distinguer le PDCA, le DMAICS, le 7Steps et le 8D/9S. Pourtant, l'apparition des réseaux distribuées de partenaires, ainsi que le positionnement du concept d'entreprise étendue, ont obligé les entreprises à aller au-delà de ses frontières pour travailler en synergie avec tous les partenaires en amont et en aval de sa chaîne. Dans ce contexte, l'efficacité de ces méthodes de résolution des problèmes a été fortement impactée. Ceci car non seulement les problèmes, mais aussi les produits, les partenaires, les ressources et l'information nécessaires pour sa résolution sont extrêmement fragmentés et décentralisés. Cette thèse s'intéresse donc à la résolution collaborative de problèmes au sein des chaînes distribuées de partenaires et son objectif est de proposer un processus et une méthodologie adaptés à ces contextes. Les propositions faites prennent en compte les aspects techniques (e.g. la modélisation des flux et la configuration de la chaîne) ainsi que les aspects collaboratifs (e.g. le niveau de confiance et/ou le rapport de pouvoir entre les partenaires) que conditionnent l'opération et l'efficacité du réseau. Finalement, cette thèse s'intéresse à l'articulation d'un système de retour d'expérience dans la résolution de problèmes distribués afin d'améliorer son efficacité. ABSTRACT : The Problem Solving Process is a central element of the firms' continuous improvement strategies. In this framework, a number of approaches have succeeded to demonstrate their effectiveness to tackle industrial problems. The list includes, but is not limited to PDCA, DMAICS, 7Steps and 8D/9S. However, the emergence and increasing emphasis in the supply chains have impacted the effectiveness of those methods to solve problems that go beyond the boundaries of a single firm and, in consequence, their ability to provide solutions when the contexts on which firms operate are distributed. This can be explained because not only the problems, but also the products, partners, skills, resources and pieces of evidence required to solve those problems are distributed, fragmented and decentralized across the network. This PhD thesis deals with the solving of industrial problems in supply chains based in collaboration. It develops a general framework for studying this paradigm, as well as both a generic process and a collaborative methodology able to deal with the process in practice. The proposal considers all the technical aspects (e.g. products modeling and network structure) and the collaborative aspects (e.g. the trust decisions and/or the power gaps between partners) that simultaneously impact the supply chain operation and the jointly solving of problems. Finally, this research work positions the experiential knowledge as a central lever of the problem solving process to contribute to the continuous improvement strategies at a more global level

Case-Based Decision Support for Disaster Management

Disasters are characterized by severe disruptions of the society’s functionality and adverse impacts on humans, the environment, and economy that cannot be coped with by society using its own resources. This work presents a decision support method that identifies appropriate measures for protecting the public in the course of a nuclear accident. The method particularly considers the issue of uncertainty in decision-making as well as the structured integration of experience and expert knowledge