989 resultados para web clustering
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Aplicación web desarrollada en PHP para la gestión de una academia: gestión de alumnos y profesores, publicación de noticias, etc. El desarrollo se ha llevado a cabo usando el Framework Code Igniter para PHP que implementa el patrón MVC; y para el desarrollo de la interfaz de usuarios se han utilizando elementos de HTML5 y CSS3, asegurando la compatibilidad con la mayor cantidad de navegadores posibles.
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Este trabajo define qué es una base de datos semántica, qué ventajas ofrece, cómo se utiliza y en qué tipo de proyectos o sistemas tiene sentido usarla. Además, en él se estudia en detalle una de ellas, OWLIM 1, de la empresa Ontotext, para evaluar la dificultad de usarla, su rendimiento y sus capacidades específicas.
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Projecte de fi de carrera d'un portal web cercador de feina realitzat amb .NET i utilitzant SQL com a repositori de dades.
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Estudio sobre los sistemas de información geográfica y cómo aplicarlos en casos específicos. Concretamente se plantea cómo aplicar los conocimientos adquiridos sobre las tecnologías SIG en la realización de un análisis y la visualización sobre un mapa interactivo de los datos proporcionados por la Agencia de Salud Pública de Barcelona.
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Estudi d'implantació d'una infraestructura de videoconferència web per reduir les despeses en el desplaçament i les dietes de qualsevol gran empresa.
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Boletín semanal para profesionales sanitarios de la Secretaría General de Calidad, Innovación y Salud Pública de la Consejería de Igualdad, Salud y Políticas Sociales
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Q?Web és un projecte de programari emmarcat dins l'àmbit de l'anomenat Technology Enhanced Learning (TEL). Aquest àrea de coneixement fa referència a l'ús de les TIC's per donar suport a qualsevol activitat d'aprenentatge.
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Este proyecto tiene como objetivo desarrollar las herramientas necesarias para poder crear un mapa conceptual de las aplicaciones de una organización, representar gráficamente este mapa y controlar el estado de cada aplicación. En concreto, se trata de desarrollar un formato XML que permita identificar y describir una aplicación, detallar con qué tecnología está desarrollada, qué componentes utiliza, especificar las interacciones o dependencias con otros sistemas, etc.
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HEMOLIA (a project under European community’s 7th framework programme) is a new generation Anti-Money Laundering (AML) intelligent multi-agent alert and investigation system which in addition to the traditional financial data makes extensive use of modern society’s huge telecom data source, thereby opening up a new dimension of capabilities to all Money Laundering fighters (FIUs, LEAs) and Financial Institutes (Banks, Insurance Companies, etc.). This Master-Thesis project is done at AIA, one of the partners for the HEMOLIA project in Barcelona. The objective of this thesis is to find the clusters in a network drawn by using the financial data. An extensive literature survey has been carried out and several standard algorithms related to networks have been studied and implemented. The clustering problem is a NP-hard problem and several algorithms like K-Means and Hierarchical clustering are being implemented for studying several problems relating to sociology, evolution, anthropology etc. However, these algorithms have certain drawbacks which make them very difficult to implement. The thesis suggests (a) a possible improvement to the K-Means algorithm, (b) a novel approach to the clustering problem using the Genetic Algorithms and (c) a new algorithm for finding the cluster of a node using the Genetic Algorithm.
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Annexos web de l'estudi que analitza -seguint una metodologia quantitativa basada en una mostra representativa de 2.093 professors i 23.864 estudiants i reforçada amb elements qualitatius- la transició que es produeix en el sistema universitari públic català cap a un model més adaptat a les noves necessitats de la societat xarxa. Per a això, es posa especial èmfasi en l'anàlisi dels usos que es fa d'Internet (l'eina clau de la societat xarxa) en el món universitari i en les transformacions que es donen o es donaran com a conseqüència d'aquests usos.
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OBJECTIVE: This study assessed clustering of multiple risk behaviors (i.e., low leisure-time physical activity, low fruits/vegetables intake, and high alcohol consumption) with level of cigarette consumption. METHODS: Data from the 2002 Swiss Health Survey, a population-based cross-sectional telephone survey assessing health and self-reported risk behaviors, were used. 18,005 subjects (8052 men and 9953 women) aged 25 years old or more participated. RESULTS: Smokers more frequently had low leisure time physical activity, low fruits/vegetables intake, and high alcohol consumption than non- and ex-smokers. Frequency of each risk behavior increased steadily with cigarette consumption. Clustering of risk behaviors increased with cigarette consumption in both men and women. For men, the odds ratios of multiple (> or =2) risk behaviors other than smoking, adjusted for age, nationality, and educational level, were 1.14 (95% confidence interval: 0.97, 1.33) for ex-smokers, 1.24 (0.93, 1.64) for light smokers (1-9 cigarettes/day), 1.72 (1.36, 2.17) for moderate smokers (10-19 cigarettes/day), and 3.07 (2.59, 3.64) for heavy smokers (> or =20 cigarettes/day) versus non-smokers. Similar odds ratios were found for women for corresponding groups, i.e., 1.01 (0.86, 1.19), 1.26 (1.00, 1.58), 1.62 (1.33, 1.98), and 2.75 (2.30, 3.29). CONCLUSIONS: Counseling and intervention with smokers should take into account the strong clustering of risk behaviors with level of cigarette consumption.
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Our essay aims at studying suitable statistical methods for the clustering ofcompositional data in situations where observations are constituted by trajectories ofcompositional data, that is, by sequences of composition measurements along a domain.Observed trajectories are known as “functional data” and several methods have beenproposed for their analysis.In particular, methods for clustering functional data, known as Functional ClusterAnalysis (FCA), have been applied by practitioners and scientists in many fields. To ourknowledge, FCA techniques have not been extended to cope with the problem ofclustering compositional data trajectories. In order to extend FCA techniques to theanalysis of compositional data, FCA clustering techniques have to be adapted by using asuitable compositional algebra.The present work centres on the following question: given a sample of compositionaldata trajectories, how can we formulate a segmentation procedure giving homogeneousclasses? To address this problem we follow the steps described below.First of all we adapt the well-known spline smoothing techniques in order to cope withthe smoothing of compositional data trajectories. In fact, an observed curve can bethought of as the sum of a smooth part plus some noise due to measurement errors.Spline smoothing techniques are used to isolate the smooth part of the trajectory:clustering algorithms are then applied to these smooth curves.The second step consists in building suitable metrics for measuring the dissimilaritybetween trajectories: we propose a metric that accounts for difference in both shape andlevel, and a metric accounting for differences in shape only.A simulation study is performed in order to evaluate the proposed methodologies, usingboth hierarchical and partitional clustering algorithm. The quality of the obtained resultsis assessed by means of several indices