RESUMO
Distributed data processing systems have become the standard means for big data analytics. These systems are based on processing pipelines where operations on data are performed in a chain of consecutive steps. Normally, the operations performed by these pipelines are set at design time, and any changes to their functionality require the applications to be restarted. This is not always acceptable, for example, when we cannot afford downtime or when a long-running calculation would lose significant progress. The introduction of variation points to distributed processing pipelines allows for on-the-fly updating of individual analysis steps. In this paper, we extend such basic variation point functionality to provide fully automated reconfiguration of the processing steps within a running pipeline through an automated planner. We have enabled pipeline modeling through constraints. Based on these constraints, we not only ensure that configurations are compatible with type but also verify that expected pipeline functionality is achieved. Furthermore, automating the reconfiguration process simplifies its use, in turn allowing users with less development experience to make changes. The system can automatically generate and validate pipeline configurations that achieve a specified goal, selecting from operation definitions available at planning time. It then automatically integrates these configurations into the running pipeline. We verify the system through the testing of a proof-of-concept implementation. The proof of concept also shows promising results when reconfiguration is performed frequently.
RESUMO
For an organization to be customer centric and service oriented requires that it use each encounter with a customer to create value, leverage advanced technologies to design digital services to fulfill the value, and assess perceived value-in-use to continue to revise the value as customer expectations evolve. The adaptation of value cycles to address the rapid changes in customer expectations requires agile digital platforms with dynamic software ecosystems interacting with multiple actors. For public health agencies focused on population health, these agile digital platforms should provide tailored care to address the distinct needs of select population groups. Using prior research on aging and dynamic software ecosystems, this paper develops a template for the design of an agile digital platform to support value cycle activities among clinical and non-clinical actors, including population groups. It illustrates the design of an agile digital platform to support clients that suffer from delirium, using digital services that leverage Internet of Things, natural language processing, and AI that uses real-time data for learning and care adaption. We conclude the paper with directions for future research.
Assuntos
Delírio , Saúde da População , Inteligência Artificial , Ecossistema , Humanos , SoftwareRESUMO
Introducción: la formación estadística de los futuros profesionales de la salud es muy importante para su posterior desarrollo profesional. Objetivo: mostrar que la utilización de herramientas dinámicas computacionales para la simulación y demostración de conceptos en la enseñanza de la estadística en actividades montadas y ejecutadas a través de la plataforma Moodle, contribuye a aumentar la comprensión de los conceptos estadísticos y mejorar la calidad de los trabajos científicos realizados por estudiantes de medicina. Material y métodos: durante el segundo semestre del curso 2013-2014, se realizó una experiencia pedagógica con dos grupos de segundo año; a un grupo se le impartió las clases utilizando las herramientas dinámicas y al otro, utilizando la forma tradicional. La variable independiente fue el uso de herramientas computacionales dinámicas montadas y ejecutadas a través de la plataforma Moodle, y las dependientes la comprensión de los conceptos estadísticos y la calidad de los trabajos científicos. Resultados: en la prueba final no hubo diferencias estadísticamente significativas entre los resultados de ambos grupos (p=0,4>0,05), aunque la cantidad de aprobados en el grupo experimental (52,9 por ciento) fue superior a los del grupo de control (47,1 por ciento). En el grupo experimental la calidad de los trabajos científicos superó al grupo de control (50 por ciento y 14,3 por ciento respectivamente). Conclusiones: el uso de las herramientas dinámicas en las clases de Estadística, así como la utilización de espacios virtuales para su aprendizaje puede contribuir a mejorar la calidad del aprendizaje de esta asignatura y una aplicación más adecuada de los conceptos estadísticos estudiados en la realización de trabajos investigativos(AU)
Introduction: the training of undergraduate medical students in statistics is very important for their professional career.Objective: showing the fact that using dynamically-computerized tools for simulating and demonstrating concepts underlying the teaching of statistics in Moodle-based activities, contributes to an increase in the comprehension of statistical concepts and improvement of scientific papers designed by these medical students.Material and methods: all across the second semester of the 2013-2014 Academic Year, some pedagogical experiment was carried out using two groups; one was teaching using dynamically-computerized tools and the other the traditional teaching method. The independent variable consisted in the use of dynamically-set computing tools, carried out using the Moodle platform, whereas the dependent (variables) was the students comprehension of statistical concepts and the quality of scientific papers. Results: the final test showed no significant statistic differences among both classes (p=0,4>0,05), even though the number of students who passed the test from the class undergoing the experiment (52,9 prcent) was higher than those in the control class (47,1 percent). The experiment-conducted class showed a better quality in scientific papers (50 percent) as compared to the other group (14,3precent). Conclusions: the results of the final test and the students scientific work proved that the use of dynamic tools applied to the teaching of statistics and the use of virtual spaces can contribute to improve learning this subject and a better application of statistical concepts in research(AU)
