http://hdl.handle.net/10071/139 Mon, 20 Apr 2026 07:13:27 GMT 2026-04-20T07:13:27Z http://hdl.handle.net/10071/28503 Título próprio: IT governance maturity patterns in Portuguese healthcare Autoria: Pereira, R.; Da Silva, M. M.; Lapão, L. Editor: De Haes; W. Van Grembergen Resumo: The pervasive use of technology in organizations to address the increased services complexity has created a critical dependency on information technology (IT) that calls to a specific focus on IT Governance (ITG). However, determining the right ITG mechanisms remains a complex endeavor. This paper uses Design Science Research and proposes an exploratory research by analyzing ITG case studies to elicit possible ITG mechanisms patterns. Six interviews were performed in Portuguese healthcare services organizations to assess the ITG practices. Our goal is to build some theories (ITG mechanisms patterns), which we believe will guide healthcare services organizations about the advisable ITG mechanisms given their specific context. We also intend to elicit conclusions regarding the most relevant ITG mechanisms for Portuguese healthcare services organizations. Additionally, a comparison is made with the financial industry to identify improvement opportunities. We finish our work with limitations, contribution and future work. Sun, 01 Jan 2017 00:00:00 GMT http://hdl.handle.net/10071/28503 2017-01-01T00:00:00Z http://hdl.handle.net/10071/27262 Título próprio: Applying advanced data analytics and machine learning to enhance the safety control of dams Autoria: Rico, J.; Barateiro, J.; Mata, J.; Antunes, A.; Cardoso, E. Editor: George A. Tsihrintzis; Maria Virvou; Evangelos Sakkopoulos,; Lakhmi C. Jain Resumo: The protection of critical engineering infrastructures is vital to today’s so- ciety, not only to ensure the maintenance of their services (e.g., water supply, energy production, transport), but also to avoid large-scale disasters. Therefore, technical and financial efforts are being continuously made to improve the safety control of large civil engineering structures like dams, bridges and nuclear facilities. This con- trol is based on the measurement of physical quantities that characterize the struc- tural behavior, such as displacements, strains and stresses. The analysis of monitor- ing data and its evaluation against physical and mathematical models is the strongest tool to assess the safety of the structural behavior. Commonly, dam specialists use multiple linear regression models to analyze the dam response, which is a well- known approach among dam engineers since the 1950s decade. Nowadays, the data acquisition paradigm is changing from a manual process, where measurements were taken with low frequency (e.g., on a weekly basis), to a fully automated process that allows much higher frequencies. This new paradigm escalates the potential of data analytics on top of monitoring data, but, on the other hand, increases data quality issues related to anomalies in the acquisition process. This chapter presents the full data lifecycle in the safety control of large-scale civil engineering infrastructures (focused on dams), from the data acquisition process, data processing and storage, data quality and outlier detection, and data analysis. A strong focus is made on the use of machine learning techniques for data analysis, where the common multiple linear regression analysis is compared with deep learning strategies, namely recur- rent neural networks. Demonstration scenarios are presented based on data obtained from monitoring systems of concrete dams under operation in Portugal. Tue, 01 Jan 2019 00:00:00 GMT http://hdl.handle.net/10071/27262 2019-01-01T00:00:00Z http://hdl.handle.net/10071/16797 Título próprio: Page rank versus katz: is the centrality algorithm choice relevant to measure user influence in Twitter? Autoria: Rosa, H.; Carvalho, J. P.; Astudillo, R.; Batista, F. Editor: Kóczy, László T.; Medina, Jesús Resumo: Microblogs, such as Twitter, have become an important socio-political analysis tool. One of the most important tasks in such analysis is the detection of relevant actors within a given topic through data mining, i.e., identifying who are the most influential participants discussing the topic. Even if there is no gold standard for such task, the adequacy of graph based centrality tools such as PageRank and Katz is well documented. In this paper, we present a case study based on a "London Riots'' Twitter database, where we show that Katz is not as adequate for the task of important actors detection since it fails to detect what we refer to as "indirect gloating'', the situation where an actor capitalizes on other actors referring to him. Mon, 01 Jan 2018 00:00:00 GMT http://hdl.handle.net/10071/16797 2018-01-01T00:00:00Z http://hdl.handle.net/10071/16796 Título próprio: Gender detection of Twitter users based on multiple information sources Autoria: Vicente, M.; Batista, F.; Carvalho, J. P. Editor: Kóczy, László T. and Medina-Moreno, Jesús; Ramírez-Poussa, Eloísa Resumo: Twitter provides a simple way for users to express feelings, ideas and opinions, makes the user generated content and associated metadata, available to the community, and provides easy-to-use web and application programming interfaces to access data. The user profile information is important for many studies, but essential information, such as gender and age, is not provided when accessing a Twitter account. However, clues about the user profile, such as the age and gender, behaviors, and preferences, can be extracted from other content provided by the user. The main focus of this paper is to infer the gender of the user from unstructured information, including the username, screen name, description and picture, or by the user generated content. We have performed experiments using an English labelled dataset containing 6.5 M tweets from 65 K users, and a Portuguese labelled dataset containing 5.8 M tweets from 58 K users. We have created four distinct classifiers, trained using a supervised approach, each one considering a group of features extracted from four different sources: user name and screen name, user description, content of the tweets, and profile picture. Features related with the activity, such as number of following and number of followers, were discarded, since these features were found not indicative of gender. A final classifier that combines the prediction of each one of the four previous individual classifiers achieves the best performance, corresponding to 93.2% accuracy for English and 96.9% accuracy for Portuguese data. Mon, 01 Jan 2018 00:00:00 GMT http://hdl.handle.net/10071/16796 2018-01-01T00:00:00Z