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
In this paper we analyze the relationship between the marketing mix and new product diffusion models. The goal is to obtain a general new product diffusion model that incorporates the classic 4Ps model of the Marketing Mix: Product, Price, Place, Promotion. An empirical study was conducted using mobile broadband adoption data in Japan. © 2022 Elsevier B.V.. All rights reserved.

Abstract
Churn and how to deal with it is an essential issue in the telecommunications sector. Within the scope of actionable knowledge, we argue that it is crucial to find effective personalized interventions that can lead to a reduction in dropouts and that, at the same time, make it possible to determine the causal effect of these interventions. Considering an intervention that encourages clients to opt for a longer-term contract for benefits, we used Uplift modeling and the Transformed Outcome Approach as a machine learning-based technique for individual-level prediction. The result is actionable profiles of persuadable customers that increase retention and strike the right balance between the campaign budget.

Abstract
The digital learning transformation brings the extension of the traditional libraries to online repositories. Learning object repositories are employed to deliver several functionalities related to the learning object's lifecycle. However, these educational resources usually are not described effectively, lacking, for example, educational metadata and learning goals. Then, metadata incompleteness limits the quality of the services, such as search and recommendation, resulting in educational objects that do not have a proper role in teaching/learning environments. This work proposes to bring an active role to all educational resources, acting on the analysis generated from the usage statistics. To achieve this goal, we created a multi-agent architecture that complements the common repository's functionalities to improve learning and teaching experiences. We intend to use this architecture on a repository focused on ocean literacy learning objects. This paper presents some steps toward this goal by enhancing, when needed, the repository to adapt itself.

Abstract
Customer retention is nowadays a challenge that requires concrete and personalized actions. Traditional data mining studies focused on predictive analytics, neglecting the business domain. This work aims to present an actionable knowledge discovery based on specific, actionable attributes and measuring of their effects. It is common to use matching, and propensity score approaches in healthcare to evaluate causality. After performing matching using the actionable attributes in this analysis, the causal effect is quantified. This work concludes that the difference between having a yearly contract versus having a monthly contract affects the churn of around 34%.