Abstract

Abstract
Os Cadernos de Investigação do Mestrado em Negócio Eletrónico publicam anualmente os artigos científicos desenvolvidos pelos alunos, em colaboração com os seus orientadores de Dissertação/Projeto/Estágio, no âmbito da unidade curricular Metodologias de Investigação e Comunicação Científica, incluindo os alunos da turma de mobilidade Erasmus.

Abstract
In the present-days complex networks modeled on real-world data contain millions of nodes and billions of links. Identifying super spreaders in such an extensive network is a challenging task. Super spreaders are the most important or influential nodes in the network that play the central role during an infection spreading or information diffusion process. Depending on the application, either the most influential node needs to be identified, or a set of initial seed nodes are identified that can maximize the collective influence or the total spread in the network. Many centrality measures have been proposed to rank nodes in a complex network such as 'degree', 'closeness', 'betweenness', 'coreness' or 'k-shell' centrality, among others. All have some kind of inherent limitations. Mixed degree decomposition or m-shell is an improvement over k-shell that yields better ranking. Many researchers have employed single node identification heuristics to select multiple seed nodes by considering top-k nodes from the ranked list. This approach does not results in the optimal seed nodeset due to the considerable overlap in total spreading influence. Influence overlap occurs when multiple nodes from the seed nodeset influence a specific node, and it is counted multiple times during total collective influence computation. In this paper, we exploit the 'node degree', 'closeness' and 'coreness' among the nodes and propose novel heuristic template to rank the super spreaders in a network. We employ k-shell and m-shell as a coreness measure in two variants for a comparative evaluation. We use a geodesic-based constraint (enforcing a minimum distance between seed nodes) to select an initial seed nodeset from that ranked nodes for influence maximization instead of selecting the top-k nodes naively. All models and metrics are updated to avoid overlapping influence during total spread computation. Experimental simulation with the SIR (Susceptible-Infectious-Recovered) spreading model and an evaluation with performance metrics like spreadability, monotonicity of ranking, Kendall's rank correlation on some benchmark real-world networks establish the superiority of the proposed methods and the improved seed node selection technique.

Abstract
Electricity has been acquiring a more significant presence in our lives, and it is estimated that the future will be increasingly electric. Nowadays, we have access to enormous amounts of data that do not have much-added value if they cannot support decision-making or plan systems in advance and correctly. Forecasts are vital tools to support decision-making. We believe it is possible to resort to open data available on the Internet to make electricity price forecasts that - decision-makers can use in the sector. In this work, we study the multi-attribute hourly forecast of the electricity price in MIBEL (Iberian electricity market) for the 24 h of the following day, using open data. The realization of the multi-attribute predictions fell on the TIM (‘Tangent Information Modeler’) tool with AutoML (‘Auto Machine Learning’) capabilities. The TOPSIS (‘technique for order of preference by similarity to ideal solution’) decision support technique was used to analyze the results. © 2021, Springer Nature Switzerland AG.

Abstract
This research aims to identify the corruption of the civil servants in the Federal District, Brazilian Public Administration. For this purpose, a predictive model was created integrating data from eight different systems and applying logistic regression to real datasets that, by their nature, present a low percentage of examples of interest in identifying patterns for machine learning, a situation defined as a class imbalance. In this study, the imbalance of classeswas considered extreme at a ratio of 1:707 or, in percentage terms, 0.14% of the interest class to the population. Two possible approaches were used, balancing with resampling techniques using synthetic minority oversampling techniqueSMOTEand applying algorithms with specific parameterization to obtain the desired standards of the minority classwithout generating bias from the dominant class. The best modeling resultwas obtained by applying it to the second approach, generating an area value on the ROC curve of around 0.69. Based on sixty-eight features, the respective coefficients that correspond to the risk factors for corruption were found. A subset of twenty features is discussed in order to find practical utility after the discovery process.

Abstract
This article describes a dataset of different services acquired by users during the period in which they are active in a sports facility as well as their behavior in terms of frequency of the sport facility itself and the type of classes they prefer to attend. Each observation in the dataset corresponds to one user, including the features of subscriptions and frequency. Data were collected between June 1st 2014 and October 31st 2019 from a database of an ERP solution operating in a sports facility in Lisbon, Portugal. From this database, it was possible to perform operations of extraction, transformation and loading into the dataset. The dataset with real data can be useful for research in areas such as customer retention, machine learning, marketing, actionable knowledge and others. Although we present real data from users of a sports facility, in order to comply the GDPR legislation, the attributes that could identify the users were removed making the data anonymized. (C) 2021 The Author(s). Published by Elsevier Inc.