Abstract

Abstract
Nowadays, Higher Education Institutions (HEIs) are faced with the crucial challenge of establishing and supervising strategies and policies that are essential for decisions in various areas and at various levels. Within this context, the importance of Business Intelligence (BI) has increased significantly, emerging as an essential tool for analysing and managing data. This BI capability enables HEIs to make more informed choices in line with their global strategies. This research focuses on developing a roadmap for the effective implementation of BI systems in HEIs. Using a Design Science Research (DSR) methodology, this work proposes a structured and adaptable roadmap that covers the key factors from the design to the implementation of BI systems in HEIs. This roadmap includes not only a reference architecture for BI systems but also a set of dashboards. The roadmap was validated through a case study at the University of Tras-os-Montes e Alto Douro (UTAD), involving exploratory analysis and feedback from experts. This study stands out for its practical and theoretical approach, offering a strategic and practical guide for the adoption of BI systems in HEIs, thus responding to a need identified in the academic literature.

Abstract
New challenges arise with the upsurge of a Big Data era. Huge volumes of data, from the most varied natures, gathered from different sources, collected in different timings, often with high associated uncertainty, make the decision-making process a harsher task. Current methods are not ready to deal with characteristics of the new problems. This paper proposes a novel data selection methodology that filters big volumes of data, so that only the most correlated information is used in the decision-making process in each given context. The proposed methodology uses a clustering algorithm, which creates sub-groups of data according to their correlation. These groups are then used to feed a forecasting process that uses the relevant data for each situation, while discarding data that is not expected to contribute to improving the forecasting results. In this way, a faster, less computationally demanding, and effective forecasting is enabled. A case study is presented, considering the application of the proposed methodology to the filtering of electricity market data used by forecasting approaches. Results show that the data selection increases the forecasting effectiveness of forecasting methods, as well as the computational efficiency of the forecasts, by using less yet more adequate data. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Over the years, industrial evolution has proved to be a complex process, since there are several aspects that need to be considered to achieve highly functional processes and differentiated quality products. To date, four industrial revolutions have been implemented. Thus, the paradigm of Industry 4.0 (I4.0) was born, a concept that aims to improve the efficiency, productivity, automation, and safety of industrial processes, but which also considers the operator’s relevance and centrality in these processes. Besides these four revolutions one more concept is emerging, called Industry 5.0 (I5.0). In recent years, and with the advance of scientific research, the implementation of wearables has proven to be the ideal solution to move towards the digitisation of Industrial sector. In this sense, the aim of this work is to provide a systematic review on the currently available knowledge about wearable technology and its applicability within I4.0. Through these technologies, both processes and operators can be monitored in real time, actively contributing to the identification of limitations and to the implementation of improvements. On the other hand, studies on the acceptance of these devices have shown a certain apprehension by users regarding the security and privacy of collected data. Therefore, studies should be conducted to analyse in depth these limitations, to raise users’ confidence and contribute, in a broader perspective, to the success of industrial processes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The trends of the 21st century are challenging the traditional production process due to the reduction in the life cycle of products and the demand for more complex products in greater quantities. Industry 4.0 (I4.0) was introduced in 2011 and it is recognized as the fourth industrial revolution, with the aim of improving manufacturing processes and increasing the competitiveness of industry. I4.0 uses technological concepts such as Cyber-Physical Systems, Internet of Things and Cloud Computing to create services, reduce costs and increase productivity. In addition, concepts such as Smart Factories are emerging, which use context awareness to assist people and optimize tasks based on data from the physical and virtual world. This article explores and applies the capabilities of context-aware applications in industry, with a focus on production lines. In specific, this paper proposes a context-aware application based on a microservices approach, intended for integration into a context-aware information system, with specific application in the area of manufacturing. The manuscript presents a detailed architecture for structuring the application, explaining components, functions and contributions. The discussion covers development technologies, integration and communication between the application and other services, as well as experimental findings, which demonstrate the applicability and advantages of the proposed solution.

Abstract
The Wi-Fi networks are more and more omnipresent in our quotidian. It is a cheap technology that is available in many places, public or private (e.g.: schools, hospitals, public transports). The vulgarization of this technology is related to the impact that the concept of Internet of Things (IoT) has been having during the last years, revolutionizing the way to interact with “things”. The use of Unmanned Vehicles (UV) tends to increase, for ludic or professional ends. These vehicles allow to assist and minimize the human intervention in many situations, but one of the associated problems settles in their communication architecture that needs the use of an ad-hoc remote controller that restricts the control area. This work intends to explore the ESP8266 microcontroller to control a UV over a Wi-Fi connection. Three architectures that support the interaction with the vehicle are presented and specified. The real test scenario validated all the described architectures to control the vehicles. © Springer Nature Switzerland AG 2019.