Abstract
EMPOWER SSE is a Fundação para a Ciência e Tecnologia (FCT, Portugal) and Department of Science & Technology (DST, India), financed research project that aims to use the Linked Open Data Framework to empower the Social and Solidarity Economy (SSE) Agents. It is a collaborative project between India and Portugal that is focused on defining a Semantic Web framework to consolidate players of the informal sector, enabling a paradigm shift. The Indian economy can be categorized into two sectors: formal and informal. The informal sector economy differs from the formal as it is an unorganized sector and comprised of economic activities that are not covered by formal arrangements such as taxation, labor protections, minimum wage regulations, unemployment benefits, or documentation. The major economy in India depends on the skilled labor of this informal sector such e.g. daily labor, farmers, electricians, food production, and small-scale industries (Kalyani, 2016). The informal sector is mainly made of skilled people that follow their family job traditions, sometimes they are not even formally trained. This sector struggles with the lack of information, data sharing needs and interoperability issues across systems and organizational boundaries. In fact, this sector does not have any visibility to the society not having the possibility to do business as most of the agents of this sector do not reach the end of the chain. This blocks them from getting proper exposure and a better livelihood.

Abstract

Abstract
Apresentamos o mais recente número da Revista de Ciências da Computação. As primeiras palavras de agradecimento vão para os autores, para os membros do conselho editorial encarregues das revisões científicas e para os revisores de língua portuguesa e inglesa. É com tristeza que retiramos o nome do falecido colega Jaime Remédios do conselho editorial. Os artigos estão organizados por ordem de chegada. O primeiro artigo, vindo na continuidade do número anterior, trata de um projeto didático para a programação paralela distribuída. O segundo artigo, de um recente mestre desta universidade, trata uma curiosa aplicação de algoritmos conhecidos na seleção de recursos humanos. O terceiro e quarto artigo, de licenciados recentes desta universidade, tratam respetivamente o planeamento de ações e a visualização de redes. Finalmente, o quinto artigo, apresenta uma nova métrica para a acessibilidade de velocípedes. Se houver solicitações por parte dos leitores, este número terá uma edição em papel disponível na Amazon com o título Revista de Ciências das Computação nº13. Entretanto, convidam-se os autores a submeter trabalhos originais em língua portuguesa ou inglesa para o próximo número da Revista das Ciências da Computação da Universidade Aberta.

Abstract
As instalações desportivas que oferecem serviços desportivos regulares têm vindo a adotar sistemas ERP e CRM, existindo atualmente bases de dados com dados históricos de grande valia. Neste trabalho demonstramos que aplicando modelos preditivos a estes dados é possível identificar perfis de abandono. Com base nos perfis encontrados é realizado um planeamento de experiências, com grupos de teste e controlo, com vista a encontrar ações concretas de fidelização.;The sports facilities that offer regular sports services have been adopting ERP and CRM systems and there are now databases with historical data of great value. In this work, we demonstrate that by applying predictive models to these data it is possible to identify abandonment profiles. Based on the profiles found, experience planning is carried out, with test and control groups, in order to find concrete actions of loyalty.

Abstract
The comprehension of social network phenomena is closely related to data visualization. However, even with only hundreds of nodes, the visualization of dense networks is usually difficult. The strategy adopted in this work is data reduction using communities. Community detection in social network analysis is a very important issue and in particular detection of community overlapping. In this approach, the information extracted from social networks transcends cohesive groups, enabling the discovery of brokers that interact among communities. To find admissible solutions in hard problems, relaxed approaches are used. Quasi-cliques are generated, and partition is found using a partial set-covering heuristic. The proposed method allows the identification of communities and actors that link two or more groups. In the visualization process, the user can choose different dimension reduction approaches for the condensed graph. For each condensed structure, a hypergraph can be drawn, identifying communities and brokers.

Abstract
Feature selection is one of the most important concepts in data mining when dimensionality reduction is needed. The performance measures of feature selection encompass predictive accuracy and result comprehensibility. Consistency-based methods are a significant category of feature selection research that substantially improves the comprehensibility of the result using the parsimony principle. In this work, the biobjective version of the algorithm logical analysis of inconsistent data is applied to large volumes of data. In order to deal with hundreds of thousands of attributes, heuristic decomposition uses parallel processing to solve a set covering problem and a cross-validation technique. The biobjective solutions contain the number of reduced features and the accuracy. The algorithm is applied to omics datasets with genome-like characteristics of patients with rare diseases.