Framework for collaborative knowledge management in organizations

Nowadays organizations have been pushed to speed up the rate of industrial transformation to high value products and services. The capability to agilely respond to new market demands became a strategic pillar for innovation, and knowledge management could support organizations to achieve that goal. However, current knowledge management approaches tend to be over complex or too academic, with interfaces difficult to manage, even more if cooperative handling is required. Nevertheless, in an ideal framework, both tacit and explicit knowledge management should be addressed to achieve knowledge handling with precise and semantically meaningful definitions. Moreover, with the increase of Internet usage, the amount of available information explodes. It leads to the observed progress in the creation of mechanisms to retrieve useful knowledge from the huge existent amount of information sources. However, a same knowledge representation of a thing could mean differently to different people and applications. Contributing towards this direction, this thesis proposes a framework capable of gathering the knowledge held by domain experts and domain sources through a knowledge management system and transform it into explicit ontologies. This enables to build tools with advanced reasoning capacities with the aim to support enterprises decision-making processes. The author also intends to address the problem of knowledge transference within an among organizations. This will be done through a module (part of the proposed framework) for domain’s lexicon establishment which purpose is to represent and unify the understanding of the domain’s used semantic

Security Management Framework for the Internet of Things

The increase in the design and development of wireless communication technologies offers multiple opportunities for the management and control of cyber-physical systems with connections between smart and autonomous devices, which provide the delivery of simplified data through the use of cloud computing. Given this relationship with the Internet of Things (IoT), it established the concept of pervasive computing that allows any object to communicate with services, sensors, people, and objects without human intervention. However, the rapid growth of connectivity with smart applications through autonomous systems connected to the internet has allowed the exposure of numerous vulnerabilities in IoT systems by malicious users. This dissertation developed a novel ontology-based cybersecurity framework to improve security in IoT systems using an ontological analysis to adapt appropriate security services addressed to threats. The composition of this proposal explores two approaches: (1) design time, which offers a dynamic method to build security services through the application of a methodology directed to models considering existing business processes; and (2) execution time, which involves monitoring the IoT environment, classifying vulnerabilities and threats, and acting in the environment, ensuring the correct adaptation of existing services. The validation approach was used to demonstrate the feasibility of implementing the proposed cybersecurity framework. It implies the evaluation of the ontology to offer a qualitative evaluation based on the analysis of several criteria and also a proof of concept implemented and tested using specific industrial scenarios. This dissertation has been verified by adopting a methodology that follows the acceptance in the research community through technical validation in the application of the concept in an industrial setting.O aumento no projeto e desenvolvimento de tecnologias de comunicação sem fio oferece múltiplas oportunidades para a gestão e controle de sistemas ciber-físicos com conexões entre dispositivos inteligentes e autônomos, os quais proporcionam a entrega de dados simplificados através do uso da computação em nuvem. Diante dessa relação com a Internet das Coisas (IoT) estabeleceu-se o conceito de computação pervasiva que permite que qualquer objeto possa comunicar com os serviços, sensores, pessoas e objetos sem intervenção humana. Entretanto, o rápido crescimento da conectividade com as aplicações inteligentes através de sistemas autônomos conectados com a internet permitiu a exposição de inúmeras vulnerabilidades dos sistemas IoT para usuários maliciosos. Esta dissertação desenvolveu um novo framework de cibersegurança baseada em ontologia para melhorar a segurança em sistemas IoT usando uma análise ontológica para a adaptação de serviços de segurança apropriados endereçados para as ameaças. A composição dessa proposta explora duas abordagens: (1) tempo de projeto, o qual oferece um método dinâmico para construir serviços de segurança através da aplicação de uma metodologia dirigida a modelos, considerando processos empresariais existentes; e (2) tempo de execução, o qual envolve o monitoramento do ambiente IoT, a classificação de vulnerabilidades e ameaças, e a atuação no ambiente garantindo a correta adaptação dos serviços existentes. Duas abordagens de validação foram utilizadas para demonstrar a viabilidade da implementação do framework de cibersegurança proposto. Isto implica na avaliação da ontologia para oferecer uma avaliação qualitativa baseada na análise de diversos critérios e também uma prova de conceito implementada e testada usando cenários específicos. Esta dissertação foi validada adotando uma metodologia que segue a validação na comunidade científica através da validação técnica na aplicação do nosso conceito em um cenário industrial

A Multi-Criteria Framework to Assist on the Design of Internet-of-Things Systems

The Internet-of-Things (IoT), considered as Internet first real evolution, has become immensely important to society due to revolutionary business models with the potential to radically improve Human life. Manufacturers are engaged in developing embedded systems (IoT Systems) for different purposes to address this new variety of application domains and services. With the capability to agilely respond to a very dynamic market offer of IoT Systems, the design phase of IoT ecosystems can be enhanced. However, select the more suitable IoT System for a certain task is currently based on stakeholder’s knowledge, normally from lived experience or intuition, although it does not mean that a proper decision is being made. Furthermore, the lack of methods to formally describe IoT Systems characteristics, capable of being automatically used by methods is also an issue, reinforced by the growth of available information directly connected to Internet spread. Contributing to improve IoT Ecosystems design phase, this PhD work proposes a framework capable of fully characterise an IoT System and assist stakeholder’s on the decision of which is the proper IoT System for a specific task. This enables decision-makers to perform a better reasoning and more aware analysis of diverse and very often contradicting criteria. It is also intended to provide methods to integrate energy consumptionsimulation tools and address interoperability with standards, methods or systems within the IoT scope. This is addressed using a model-driven based framework supporting a high openness level to use different software languages and decision methods, but also for interoperability with other systems, tools and methods

Software framework for the development of context-aware reconfigurable systems

In this project we propose a new software framework for the development of context-aware and secure controlling software of distributed reconfigurable systems. Context-awareness is a key feature allowing the adaptation of systems behaviour according to the changing environment. We introduce a new definition of the term “context” for reconfigurable systems then we define a new context modelling and reasoning approach. Afterwards, we define a meta-model of context-aware reconfigurable applications that paves the way to the proposed framework. The proposed framework has a three-layer architecture: reconfiguration, context control, and services layer, where each layer has its well-defined role. We define also a new secure conversation protocol between distributed trustless parts based on the blockchain technology as well as the elliptic curve cryptography. To get better correctness and deployment guarantees of applications models in early development stages, we propose a new UML profile called GR-UML to add new semantics allowing the modelling of probabilistic scenarios running under memory and energy constraints, then we propose a methodology using transformations between the GR-UML, the GR-TNCES Petri nets formalism, and the IEC 61499 function blocks. A software tool implementing the methodology concepts is developed. To show the suitability of the mentioned contributions two case studies (baggage handling system and microgrids) are considered.In diesem Projekt schlagen wir ein Framework für die Entwicklung von kontextbewussten, sicheren Anwendungen von verteilten rekonfigurierbaren Systemen vor. Kontextbewusstheit ist eine Schlüsseleigenschaft, die die Anpassung des Systemverhaltens an die sich ändernde Umgebung ermöglicht. Wir führen eine Definition des Begriffs ``Kontext" für rekonfigurierbare Systeme ein und definieren dann einen Kontextmodellierungs- und Reasoning-Ansatz. Danach definieren wir ein Metamodell für kontextbewusste rekonfigurierbare Anwendungen, das den Weg zum vorgeschlagenen Framework ebnet. Das Framework hat eine dreischichtige Architektur: Rekonfigurations-, Kontextkontroll- und Dienste-Schicht, wobei jede Schicht ihre wohldefinierte Rolle hat. Wir definieren auch ein sicheres Konversationsprotokoll zwischen verteilten Teilen, das auf der Blockchain-Technologie sowie der elliptischen Kurven-Kryptographie basiert. Um bessere Korrektheits- und Einsatzgarantien für Anwendungsmodelle zu erhalten, schlagen wir ein UML-Profil namens GR-UML vor, um Semantik umzufassen, die die Modellierung probabilistischer Szenarien unter Speicher- und Energiebeschränkungen ermöglicht. Dann schlagen wir eine Methodik vor, die Transformationen zwischen GR-UML, dem GR-TNCES-Petrinetz-Formalismus und den IEC 61499-Funktionsblöcken verwendet. Es wird ein Software entwickelt, das die Konzepte der Methodik implementiert. Um die Eignung der genannten Beiträge zu zeigen, werden zwei Fallstudien betrachtet

Digital transformation in the manufacturing industry : business models and smart service systems

The digital transformation enables innovative business models and smart services, i.e. individual services that are based on data analyses in real-time as well as information and communications technology. Smart services are not only a theoretical construct but are also highly relevant in practice. Nine research questions are answered, all related to aspects of smart services and corresponding business models. The dissertation proceeds from a general overview, over the topic of installed base management as precondition for many smart services in the manufacturing industry, towards exemplary applications in form of predictive maintenance activities. A comprehensive overview is provided about smart service research and research gaps are presented that are not yet closed. It is shown how a business model can be developed in practice. A closer look is taken on installed base management. Installed base data combined with condition monitoring data leads to digital twins, i.e. dynamic models of machines including all components, their current conditions, applications and interaction with the environment. Design principles for an information architecture for installed base management and its application within a use case in the manufacturing industry indicate how digital twins can be structured. In this context, predictive maintenance services are taken for the purpose of concretization. It is looked at state oriented maintenance planning and optimized spare parts inventory as exemplary approaches for smart services that contribute to high machine availability. Taxonomy of predictive maintenance business models shows their diversity. It is viewed on the named topics both from theoretical and practical viewpoints, focusing on the manufacturing industry. Established research methods are used to ensure academic rigor. Practical problems are considered to guarantee practical relevance. A research project as background and the resulting collaboration with different experts from several companies also contribute to that. The dissertation provides a comprehensive overview of smart service topics and innovative business models for the manufacturing industry, enabled by the digital transformation. It contributes to a better understanding of smart services in theory and practice and emphasizes the importance of innovative business models in the manufacturing industry

Methodological approaches and techniques for designing ontologies in information systems requirements engineering

Programa doutoral em Information Systems and TechnologyThe way we interact with the world around us is changing as new challenges arise, embracing innovative business models, rethinking the organization and processes to maximize results, and evolving change management. Currently, and considering the projects executed, the methodologies used do not fully respond to the companies' needs. On the one hand, organizations are not familiar with the languages used in Information Systems, and on the other hand, they are often unable to validate requirements or business models. These are some of the difficulties encountered that lead us to think about formulating a new approach. Thus, the state of the art presented in this paper includes a study of the models involved in the software development process, where traditional methods and the rivalry of agile methods are present. In addition, a survey is made about Ontologies and what methods exist to conceive, transform, and represent them. Thus, after analyzing some of the various possibilities currently available, we began the process of evolving a method and developing an approach that would allow us to design ontologies. The method we evolved and adapted will allow us to derive terminologies from a specific domain, aggregating them in order to facilitate the construction of a catalog of terminologies. Next, the definition of an approach to designing ontologies will allow the construction of a domain-specific ontology. This approach allows in the first instance to integrate and store the data from different information systems of a given organization. In a second instance, the rules for mapping and building the ontology database are defined. Finally, a technological architecture is also proposed that will allow the mapping of an ontology through the construction of complex networks, allowing mapping and relating terminologies. This doctoral work encompasses numerous Research & Development (R&D) projects belonging to different domains such as Software Industry, Textile Industry, Robotic Industry and Smart Cities. Finally, a critical and descriptive analysis of the work done is performed, and we also point out perspectives for possible future work.A forma como interagimos com o mundo à nossa volta está a mudar à medida que novos desafios surgem, abraçando modelos empresariais inovadores, repensando a organização e os processos para maximizar os resultados, e evoluindo a gestão da mudança. Atualmente, e considerando os projetos executados, as metodologias utilizadas não respondem na totalidade às necessidades das empresas. Por um lado, as organizações não estão familiarizadas com as linguagens utilizadas nos Sistemas de Informação, por outro lado, são muitas vezes incapazes de validar requisitos ou modelos de negócio. Estas são algumas das dificuldades encontradas que nos levam a pensar na formulação de uma nova abordagem. Assim, o estado da arte apresentado neste documento inclui um estudo dos modelos envolvidos no processo de desenvolvimento de software, onde os métodos tradicionais e a rivalidade de métodos ágeis estão presentes. Além disso, é efetuado um levantamento sobre Ontologias e quais os métodos existentes para as conceber, transformar e representar. Assim, e após analisarmos algumas das várias possibilidades atualmente disponíveis, iniciou-se o processo de evolução de um método e desenvolvimento de uma abordagem que nos permitisse conceber ontologias. O método que evoluímos e adaptamos permitirá derivar terminologias de um domínio específico, agregando-as de forma a facilitar a construção de um catálogo de terminologias. Em seguida, a definição de uma abordagem para conceber ontologias permitirá a construção de uma ontologia de um domínio específico. Esta abordagem permite em primeira instância, integrar e armazenar os dados de diferentes sistemas de informação de uma determinada organização. Num segundo momento, são definidas as regras para o mapeamento e construção da base de dados ontológica. Finalmente, é também proposta uma arquitetura tecnológica que permitirá efetuar o mapeamento de uma ontologia através da construção de redes complexas, permitindo mapear e relacionar terminologias. Este trabalho de doutoramento engloba inúmeros projetos de Investigação & Desenvolvimento (I&D) pertencentes a diferentes domínios como por exemplo Indústria de Software, Indústria Têxtil, Indústria Robótica e Smart Cities. Finalmente, é realizada uma análise critica e descritiva do trabalho realizado, sendo que apontamos ainda perspetivas de possíveis trabalhos futuros

A Smart Products Lifecycle Management (sPLM) Framework - Modeling for Conceptualization, Interoperability, and Modularity

Autonomy and intelligence have been built into many of today’s mechatronic products, taking advantage of low-cost sensors and advanced data analytics technologies. Design of product intelligence (enabled by analytics capabilities) is no longer a trivial or additional option for the product development. The objective of this research is aimed at addressing the challenges raised by the new data-driven design paradigm for smart products development, in which the product itself and the smartness require to be carefully co-constructed. A smart product can be seen as specific compositions and configurations of its physical components to form the body, its analytics models to implement the intelligence, evolving along its lifecycle stages. Based on this view, the contribution of this research is to expand the “Product Lifecycle Management (PLM)” concept traditionally for physical products to data-based products. As a result, a Smart Products Lifecycle Management (sPLM) framework is conceptualized based on a high-dimensional Smart Product Hypercube (sPH) representation and decomposition. First, the sPLM addresses the interoperability issues by developing a Smart Component data model to uniformly represent and compose physical component models created by engineers and analytics models created by data scientists. Second, the sPLM implements an NPD3 process model that incorporates formal data analytics process into the new product development (NPD) process model, in order to support the transdisciplinary information flows and team interactions between engineers and data scientists. Third, the sPLM addresses the issues related to product definition, modular design, product configuration, and lifecycle management of analytics models, by adapting the theoretical frameworks and methods for traditional product design and development. An sPLM proof-of-concept platform had been implemented for validation of the concepts and methodologies developed throughout the research work. The sPLM platform provides a shared data repository to manage the product-, process-, and configuration-related knowledge for smart products development. It also provides a collaborative environment to facilitate transdisciplinary collaboration between product engineers and data scientists

Trusted Artificial Intelligence in Manufacturing; Trusted Artificial Intelligence in Manufacturing

The successful deployment of AI solutions in manufacturing environments hinges on their security, safety and reliability which becomes more challenging in settings where multiple AI systems (e.g., industrial robots, robotic cells, Deep Neural Networks (DNNs)) interact as atomic systems and with humans. To guarantee the safe and reliable operation of AI systems in the shopfloor, there is a need to address many challenges in the scope of complex, heterogeneous, dynamic and unpredictable environments. Specifically, data reliability, human machine interaction, security, transparency and explainability challenges need to be addressed at the same time. Recent advances in AI research (e.g., in deep neural networks security and explainable AI (XAI) systems), coupled with novel research outcomes in the formal specification and verification of AI systems provide a sound basis for safe and reliable AI deployments in production lines. Moreover, the legal and regulatory dimension of safe and reliable AI solutions in production lines must be considered as well. To address some of the above listed challenges, fifteen European Organizations collaborate in the scope of the STAR project, a research initiative funded by the European Commission in the scope of its H2020 program (Grant Agreement Number: 956573). STAR researches, develops, and validates novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project researches and delivers approaches that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. This book is co-authored by the STAR consortium members and provides a review of technologies, techniques and systems for trusted, ethical, and secure AI in manufacturing. The different chapters of the book cover systems and technologies for industrial data reliability, responsible and transparent artificial intelligence systems, human centered manufacturing systems such as human-centred digital twins, cyber-defence in AI systems, simulated reality systems, human robot collaboration systems, as well as automated mobile robots for manufacturing environments. A variety of cutting-edge AI technologies are employed by these systems including deep neural networks, reinforcement learning systems, and explainable artificial intelligence systems. Furthermore, relevant standards and applicable regulations are discussed. Beyond reviewing state of the art standards and technologies, the book illustrates how the STAR research goes beyond the state of the art, towards enabling and showcasing human-centred technologies in production lines. Emphasis is put on dynamic human in the loop scenarios, where ethical, transparent, and trusted AI systems co-exist with human workers. The book is made available as an open access publication, which could make it broadly and freely available to the AI and smart manufacturing communities