Métricas de gestión : Caso práctico: Métricas para un proyecto de implantación de un core bancario

En este trabajo final de carrera se explica cómo implementar, siguiendo la metodología GQM, un sistema de métricas que ayuden a la gestión de un proyecto de implantación de un proyecto tecnológico. Además de la metodología GQM se explica el uso de algunas métricas que han sido probadas empíricamente y publicadas en la literatura científica. Otro aspecto importante que se trata en este TFC es el de la estimación basada en el método de estimación por puntos de función para realizar la estimación de costos, recursos y tiempo.

Métricas de productividad del software para la gestión de proyectos

En la preparación de todo proyecto existe una estimación de costes de los diferentes puntos a realizar. Las métricas del software pueden ser de: productividad, calidad, técnicas, orientadas al tamaño, orientadas a la función u orientadas a la persona. Este documento tratará sobre las métricas del software, que se centran en el rendimiento del proceso de la ingeniería del software.

@neurIST infrastructure for advanced disease management through integration of heterogeneous data, computing, and complex processing services

The increasing volume of data describing humandisease processes and the growing complexity of understanding, managing, and sharing such data presents a huge challenge for clinicians and medical researchers. This paper presents the@neurIST system, which provides an infrastructure for biomedical research while aiding clinical care, by bringing together heterogeneous data and complex processing and computing services. Although @neurIST targets the investigation and treatment of cerebral aneurysms, the system’s architecture is generic enough that it could be adapted to the treatment of other diseases.Innovations in @neurIST include confining the patient data pertaining to aneurysms inside a single environment that offers cliniciansthe tools to analyze and interpret patient data and make use of knowledge-based guidance in planning their treatment. Medicalresearchers gain access to a critical mass of aneurysm related data due to the system’s ability to federate distributed informationsources. A semantically mediated grid infrastructure ensures that both clinicians and researchers are able to seamlessly access andwork on data that is distributed across multiple sites in a secure way in addition to providing computing resources on demand forperforming computationally intensive simulations for treatment planning and research.

Multi-level SLA-driven resource management on Cloud Providers

Nowadays, service providers in the Cloud offer complex services ready to be used as it was a commodity like water or electricity to their customers with any other extra effort for them. However, providing these services implies a high management effort which requires a lot of human interaction. Furthermore, an efficient resource management mechanism considering only provider's resources is, though necessary, not enough, because the provider's profit is limited by the amount of resources it owns. Dynamically outsourcing resources to other providers in response to demand variation avoids this problem and makes the provider to get more profit. A key technology for achieving these goals is virtualization which facilitates provider's management and provides on-demand virtual environments, which are isolated and consolidated in order to achieve a better utilization of the provider's resources. Nevertheless, dealing with some virtualization capabilities implies an effort for the user in order to take benefit from them. In order to avoid this problem, we are contributing the research community with a virtualized environment manager which aims to provide virtual machines that fulfils with the user requirements. Another challenge is sharing resources among different federated Cloud providers while exploiting the features of virtualization in a new approach for facilitating providers' management. This project aims for reducing provider's costs and at the same time fulfilling the quality of service agreed with the customers while maximizing the provider's revenue. It considers resource management at several layers, namely locally to each node in the provider, among different nodes in the provider, and among different federated providers. This latter layer supports the novel capabilities of outsourcing when the local resources are not enough to fulfil the users demand, and offering resources to other providers when the local resources are underused.

Knowledge management for systems biology a general and visually driven framework applied to translational medicine

Background: To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype - phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory. Results: To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data. Conclusions: We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene - disease and gene - compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.