The Internet of Things (IoT) has become one of the most widely research paradigms, having received much attention from the research community in the last few years. IoT is the paradigm that creates an internet-connected world, where all the everyday objects capture data from our environment and adapt it to our needs. However, the implementation of IoT is a challenging task and all the implementation scenarios require the use of different technologies and the emergence of new ones, such as Edge Computing (EC). EC allows for more secure and efficient data processing in real time, achieving better performance and results. Energy efficiency is one of the most interesting IoT scenarios. In this scenario sensors, actuators and smart devices interact to generate a large volume of data associated with energy consumption. This work proposes the use of an Edge-IoT platform and a Social Computing framework to build a system aimed to smart energy efficiency in a public building scenario. The system has been evaluated in a public building and the results make evident the notable benefits that come from applying Edge Computing to both energy efficiency scenarios and the framework itself. Those benefits included reduced data transfer from the IoT-Edge to the Cloud and reduced Cloud, computing and network resource costs.

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Data warehouses (DW) integrate different data sources in order to give a multidimensional view of them to the decisionmaker. To this aim, the ETL (Extraction, Transformation and Load ) processes are responsible for extracting data from heterogeneous operational data sources, their transformation (conversion, cleaning, standardization, etc.), and its load in the DW. In recent years, several conceptual modeling approaches have been proposed for designing ETL processes. Although these approaches are very useful for documenting ETL processes and supporting the designer tasks, these proposals fail to give mechanisms to carry out an automatic code generation stage. Such a stage should be required to both avoid fails and save development time in the implementation of complex ETL process. Therefore, in this paper we define an approach for the automatic code generation of ETL processes. To this aim, we align the modeling of ETL processes in DW with MDA (Model Driven Architecture) by formally defining a set of QVT (Query, View, Transformation) transformations.

Agricultural Big Data is a set of technologies that allows responding to the challenges of the new data era. In conjunction with machine learning, farmers can use data to address problems such as farmers’ decision making, water management, soil management, crop management, and livestock management. Crop management includes yield prediction, disease detection, weed detection, crop quality, and species recognition. On the other hand, livestock management considers animal welfare and livestock production. The purpose of this paper is to synthesize the evidence regarding the challenges involved in implementing machine learning in agricultural Big Data. We conducted a systematic literature review applying the PRISMA protocol. This review includes 30 papers published from 2015 to 2020. We develop a framework that summarizes the main challenges encountered, machine learning techniques, and the leading technologies used. A significant challenge is the design of agricultural Big Data architectures due to the need to modify the set of technologies adapting the machine learning techniques as the volume of data increases.

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The article is the product of the study “Development of innovative resources to improve logical-mathematical skills in primary school, through educational robotics”, developed during the 2019 school year in three public schools in the province of Chiriquí, Republic of Panama. The teaching-learning process in students is influenced by aspects inside and outside the classroom, since not all schools have the necessary resources to deliver content or teaching material. The general objective of the project is to design, develop and implement educational robotics to improve logical-mathematical skills aimed at preschool and first grade students in public schools, using programmable educational robots. For this, a set of resources and activities were developed to improve the logical-mathematical skills of the initial stages, in public schools, obtaining significant results. Playful activities favor the teaching-learning process. Considering the analysis of the results made on the data obtained through the applied collection instruments, it can be argued that in general terms the values indicate that the students obtained a favorable level of performance in the different challenges proposed. The project has allowed the academic community to have an application of great value that allows teaching about the conservation of natural sites. The project only covers the area of mathematics in preschool and first grade.

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En este artículo se presenta y analiza la robótica educativa como una herramienta de apoyo al proceso de enseñanza-aprendizaje, a nivel de pre-media, orientada principalmente a asignaturas complejas como la matemática, física e informática, entre otras. El estudio se limita a los colegios secundarios de la Provincia de Chiriquí, República de Panamá; se tomó una muestra de seis colegios de la provincia y por cada colegio participaron tanto estudiantes como docentes. El objetivo principal del proyecto fue demostrar como la robótica aplicada a la educación, facilita y motiva la enseñanza-aprendizaje de las ciencias y las tecnologías.  Los resultados demostraron que la robótica se puede convertir en una herramienta excelente para comprender conceptos abstractos y complejos en asignaturas del área de las ciencias y las tecnologías; así como también permite desarrollar competencias básicas tales como trabajar en equipo.