Abstract
An integrated view of the information system has been an objective to deal with complexity. However, bibliography proposes many solutions with many synonyms depending on the layer, methodology, framework or tool used, that does not allow a broad view of the system. In this work we chose three basic elements of the information systems and we demonstrate how they are enough to integrate a set of essential UML diagrams. The proposed model firstly defines a set of UML diagrams for each layer of the Enterprise Architecture, and then heuristic rules are detailed in order to ensure vertical and horizontal alignment. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
O presente artigo estuda a influência dos fatores socioeconómicos dos diferentes municípios no sucesso educacional dos estudantes. Para verificar a existência de fatores relevantes para o percurso académico dos estudantes, foram obtidos datasets com descritores socioeconómicos por município, médias das notas dos exames nacionais e as taxas de sucesso dos alunos. Estes datasets foram submetidos a uma técnica de K-nearest neighbours para permitir encontrar valores de atributos em municípios com valores em falta. Foram, de seguida, aplicados algoritmos de classificação, através de árvores de decisão e regressão, que permitiram analisar quais os atributos socioeconómicos que tinham, potencialmente, maior relação com o sucesso escolar. O trabalho efetuado permite identificar alguns fatores como alvos de potenciais estudos futuros sem, no entanto, se verificar correlações fortes com nenhum atributo socioeconómico.;This paper studies the influence of the socio-economic factors of different municipalities on the educational success of students. To verify the existence of relevant factors to the academic course of the students, datasets were obtained with socio-economic descriptors by municipality, average grades of national exams and success rates of students. These datasets were submitted to a K-nearest neighbours technique to allow finding attributes in municipalities with missing values. Classification algorithms were then applied through decision and regression trees, which allowed analyzing which socio-economic attributes were potentially more related to school success. The work performed allowed identifying some factors as targets of potential future studies without, however, verifying strong correlations with any socio-economic attribute.

Abstract
Tendo por base um conjunto de dados dos clientes de uma empresa de produtos alimentares, tentamos implementar duas estratégias de data mining com o objetivo de compreender quais os atributos que melhor podem segmentar estes consumidores. Aplicamos primeiro um algoritmo de segmentação (k-means) para agrupar estes clientes e, seguidamente, utilizamos um algoritmo de classificação (árvore de decisão) para análise visual dos atributos que definiram os clusters da segmentação. Através da análise visual dos gráficos resultantes da indução de árvores de decisão conseguimos verificar que só o valor do salário dos clientes pode segmentar este conjunto de dados.;From a dataset of customers of a food company, we tried to implement two data mining strategies to understand which attributes can best segment these consumers. First, we applied a segmentation algorithm (k-means) to segment these customers and then we applied a classification algorithm (decision tree) for visual analysis of the attributes that defined the segmentation clusters. Through the visual analysis of the graphs resulting from the decision tree induction, we were able to verify that only the value of the customers' salary can segment this dataset.

Abstract
Judea Pearl's ladder of causation framework has dramatically influenced the understanding of causality in computer science. Despite artificial intelligence (AI) advancements, grasping causal relationships remains challenging, emphasizing the causal revolution's significance in improving AI's understanding of cause and effect. The work presents a novel taxonomy of causal inference methods, clarifying diverse approaches for inferring causality from data. It highlights the implications of causality in responsible AI and explainable AI (xAI), addressing bias in AI systems. The chapter points out causality as the next step in AI for creating new questions, developing causal tools, and clarifying opaque models with xAI approaches. The work clarifies causal models' significance and implications in various AI subareas. © 2023, IGI Global. All rights reserved.

Abstract
Open and networked education at Portuguese Open University is made up of a set of idiosyncrasies that are not immediately perceptible by new professors coming from face-to-face universities or by evaluators from A3ES (Agency for the Evaluation and Accreditation of Higher Education). This work presents the essential concepts of Distance Education with the fewest words and avoiding synonyms. In the teaching-learning process, the teacher’s role in digital contexts is detailed and the notion of learning is discussed. A teaching taxonomy is presented with an emphasis on practice in virtual communities. © 2023, Edicoes Universitarias Lusofonas. All rights reserved.

Abstract
Data quality is essential for a correct understanding of the concepts they represent. Data mining is especially relevant when data with inferior quality is used in algorithms that depend on correct data to create accurate models and predictions. In this work, we introduce the issue of errors of identifiers in an anonymous database. The work proposes a quality evaluation approach that considers individual attributes and a contextual analysis that allows additional quality evaluations. The proposed quality analysis model is a robust means of minimizing anonymization costs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.