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

Knowledge visualization: From theory to practice

Visualizations have been known as efficient tools that can help users analyze com- plex data. However, understanding the displayed data and finding underlying knowl- edge is still difficult. In this work, a new approach is proposed based on understanding the definition of knowledge. Although there are many definitions used in different ar- eas, this work focuses on representing knowledge as a part of a visualization and showing the benefit of adopting knowledge representation. Specifically, this work be- gins with understanding interaction and reasoning in visual analytics systems, then a new definition of knowledge visualization and its underlying knowledge conversion processes are proposed. The definition of knowledge is differentiated as either explicit or tacit knowledge. Instead of directly representing data, the value of the explicit knowledge associated with the data is determined based on a cost/benefit analysis. In accordance to its importance, the knowledge is displayed to help the user under- stand the complex data through visual analytical reasoning and discovery

Responsible AI and Analytics for an Ethical and Inclusive Digitized Society

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Automatic generation of software interfaces for supporting decisionmaking processes. An application of domain engineering & machine learning

[EN] Data analysis is a key process to foster knowledge generation in particular domains or fields of study. With a strong informative foundation derived from the analysis of collected data, decision-makers can make strategic choices with the aim of obtaining valuable benefits in their specific areas of action. However, given the steady growth of data volumes, data analysis needs to rely on powerful tools to enable knowledge extraction. Information dashboards offer a software solution to analyze large volumes of data visually to identify patterns and relations and make decisions according to the presented information. But decision-makers may have different goals and, consequently, different necessities regarding their dashboards. Moreover, the variety of data sources, structures, and domains can hamper the design and implementation of these tools. This Ph.D. Thesis tackles the challenge of improving the development process of information dashboards and data visualizations while enhancing their quality and features in terms of personalization, usability, and flexibility, among others. Several research activities have been carried out to support this thesis. First, a systematic literature mapping and review was performed to analyze different methodologies and solutions related to the automatic generation of tailored information dashboards. The outcomes of the review led to the selection of a modeldriven approach in combination with the software product line paradigm to deal with the automatic generation of information dashboards. In this context, a meta-model was developed following a domain engineering approach. This meta-model represents the skeleton of information dashboards and data visualizations through the abstraction of their components and features and has been the backbone of the subsequent generative pipeline of these tools. The meta-model and generative pipeline have been tested through their integration in different scenarios, both theoretical and practical. Regarding the theoretical dimension of the research, the meta-model has been successfully integrated with other meta-model to support knowledge generation in learning ecosystems, and as a framework to conceptualize and instantiate information dashboards in different domains. In terms of the practical applications, the focus has been put on how to transform the meta-model into an instance adapted to a specific context, and how to finally transform this later model into code, i.e., the final, functional product. These practical scenarios involved the automatic generation of dashboards in the context of a Ph.D. Programme, the application of Artificial Intelligence algorithms in the process, and the development of a graphical instantiation platform that combines the meta-model and the generative pipeline into a visual generation system. Finally, different case studies have been conducted in the employment and employability, health, and education domains. The number of applications of the meta-model in theoretical and practical dimensions and domains is also a result itself. Every outcome associated to this thesis is driven by the dashboard meta-model, which also proves its versatility and flexibility when it comes to conceptualize, generate, and capture knowledge related to dashboards and data visualizations

Information between Data and Knowledge: Information Science and its Neighbors from Data Science to Digital Humanities

Digital humanities as well as data science as neighboring fields pose new challenges and opportunities for information science. The recent focus on data in the context of big data and deep learning brings along new tasks for information scientist for example in research data management. At the same time, information behavior changes in the light of the increasing digital availability of information in academia as well as in everyday life. In this volume, contributions from various fields like information behavior and information literacy, information retrieval, digital humanities, knowledge representation, emerging technologies, and information infrastructure showcase the development of information science research in recent years. Topics as diverse as social media analytics, fake news on Facebook, collaborative search practices, open educational resources or recent developments in research data management are some of the highlights of this volume. For more than 30 years, the International Symposium of Information Science has been the venue for bringing together information scientists from the German speaking countries. In addition to the regular scientific contributions, six of the best competitors for the prize for the best information science master thesis present their work