Abstract
The Business School of the Polytechnic of Porto, Portugal aiming at following the demands of the region decided to make available a master's degree program in e-business. This paper describes the study held ascertain the most relevant skills to be considered in the master program. In order to obtain relevant feedback, an interview was conducted to professionals working in the field. Also, a questionnaire was applied to the students attending the last year of undergraduate after working programs, since they have already professional experience in related fields. The most relevant skills were identified, and curriculum was defined for the master's degree according to the analysis of the results of these activities.

Abstract
The aim of this chapter is to present a domain model that represents the informational needs of transparency (governance structure and accountability dimensions) in Portuguese cooperatives. A domain model is an abstract representation of a reality and a milestone in the development of a metadata application profile (MAP). A community of practice publishes linked open MAP-based data for these data to be interoperable; this means intelligent software/agents can aggregate these data, provide different types of visualizations, infer from the data, and ultimately provide new discoveries. This model was developed having as basis the information obtained from the accomplishment of a focus group, and the analysis of financial reports and websites of seven Portuguese cooperatives. The authors will continue to work on the domain model to include 1) other dimensions that also contribute for transparency in the organizations and 2) other types of entities of the social economy (SE). The final aim is to define a model representing the needs of transparency of all types of European SE entities.

Abstract
The General Data Protection Regulation 2016/679 (GDPR) is a set of legal rules to attain the privacy of people in the handling of their personal data and the movement of such data across countries. When those rules are considered in the operation of information systems, the one becomes attainable for legal approval within that scope. This paper presents a model we are developing to help enterprises do align their information system with the GDPR requirements. The model shall serve the purpose of analyzing the enterprises in what concerns the use of the subject’s personal data, allowing to capture and improve data protection capabilities placed in the GDPR. The main issue of our approach is to set a baseline to define the requirements for establishing, implementing, maintaining and continually improving data protection management system on organizations.

Abstract
Relational databases are supported by very well established models. However, some neglected problems can occur with the join operator: semantic mistakes caused by the multiple access path problem and faults when connection traps arise. In this paper we intend to identify and overcome those problems and to establish rules for relational data denormalization. Two denormalization forms are proposed and a case study is presented. © 2019, Springer Nature Switzerland AG.

Abstract
O tratamento de conjuntos de dados de grande dimensão é uma questão que é recorrente nos dias de hoje. Uma das abordagens possíveis passa por realizar uma seleção de atributos que permita diminuir, consideravelmente, a dimensão dos dados sem aumentar a inconsistência dos mesmos. A Análise Lógica de Dados Inconsistentes (LAID) é uma metodologia sistematizada, robusta, sendo fácil de interpretar e consegue lidar com dados inconsistentes. O paradigma, relativamente ao manuseamento de grandes volumes de dados, tem-se alterado. Antes, o tratamento dos dados era efetuado num único computador e o acesso era realizado depois do seu carregamento em memória. A tendência atual é aceder aos dados em disco, num ambiente cloud. Este trabalho pretende validar o novo paradigma, com recurso ao sistema de dados HDF5 e ao ambiente remoto disponibilizado pela. Pelo facto de o HDF5 ser o sistema adotado pela comunidade Python para lidar com dados de grande dimensão, esta linguagem foi escolhida para implementação do LAID.;The treatment of large datasets is an issue that is often addressed today and whose task is not simple, given the computational limitations that still exist.One possible approach is to perform a feature selection that allows a considerably reduction of data size without increasing inconsistency. Logical Analysis of Inconsistent Data (LAID) is a systematic, robust methodology that is easy to interpret and can handle inconsistent data.The paradigm regarding the handling of large data has hasbeen changing over. Previously, data processing was performed on a single computer, with in-memory data access. The current trend is to access data on disk, in a cloud environment. The present work intends to validate this new paradigm, using HDF5 data system and remote environment provided by INCD. Because HDF5 is the system adopted by Python’s community to handle large datasets, this language was chosen for LAID algorithm implementation.

Abstract
O obxectivo deste traballo é facilitar e acelerar o cálculo do nivel de orde e a súa cantidade óptima, nun sistema de xestión de stock con demanda e tempo de entrega aleatorios. Este cálculo era tradicionalmente laborioso e lento, xa que requiría un proceso iterativo de consulta de dúas táboas en cada iteración. Polo tanto, o problema solucionouse inicialmente en follas de cálculo e posteriormente ampliouse a una aplicación en la Internet, onde o usuario fornece os datos e recibe os resultados.