49 research outputs found

    Metodología de reingeniería del software para la remodelación de aplicaciones científicas heredadas

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    La reingeniería de sistemas heredados tiene por finalidad reestructurar o transformar viejossistemas en aplicaciones más fáciles de mantener, con entornos más agradables e integradas ennuevas plataformas de hardware/software. Tratamos de dar una visión general de lo que es lareingeniería de software y cuáles son las actitudes que se pueden tomar a la hora de modernizarun sistema legado que se ha quedado obsoleto en cuanto a operatividad, aspecto y software debase sobre el que se ejecuta, pero de probada eficiencia y que mantiene su funcionalidad.Mostramos las características de un proceso de desarrollo que se adapta a este tipo deaplicaciones, verificado, mediante el caso de estudio, la transformación de una aplicación escritaen un lenguaje imperativo, no estructurado, a un nuevo lenguaje visual y orientado a objetos,describiendo las diversas fases de la metodología aplicadas a un caso concreto.Legacy Systems Reengineering has as primary goal to restructure and transform old systems ineasier to maintain applications with nicer and more integrated environments that they run innew platforms of hardware/software. We try to give a general view of what is the softwarereengineering and which are the approaches that can be taken when modernizing a legacysystem that has been obsolete as for operability, but of proved efficiency and that maintains itsfunctionality. We show the characteristics of a development process that adapts itself to thistype of systems, verified by means of the case of study, the transformation of an applicationwritten in an imperative language, no structured, to a new visual and object oriented language,describing the different stages of the methodology applied to a particular case

    Multiclassifier Systems for Predicting Neurological Outcome of Patients with Severe Trauma and Polytrauma in Intensive Care Units

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    [EN]This paper presents an ensemble based classification proposal for predicting neurological outcome of severely traumatized patients. The study comprises both the whole group of patients and a subgroup containing those patients suffering traumatic brain injury (TBI). Data was gathered from patients hospitalized in the Intensive Care Unit (ICU) of the University Hospital in Salamanca. Predictive models were induced from both epidemiologic and clinical variables taken at the emergency room and along the stay in the ICU. The large number of variables leads to a low accuracy in the classifiers even when feature selection methods are used. In addition, the presence of a much larger number of instances of one of the classes in the subgroup of TBI patients produces a significantly lesser precision for the minority class
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