An Enhanced Data Mining Life Cycle

Data mining projects are complex and can have a high failure rate. In order to improve project management and success rates of such projects a life cycle is vital to the overall success of the project. This paper reports on a research project that was concerned with the life cycle development for data mining projects, its team members and their role. The paper provides a detailed view of the design and development of the data mining life cycle called DMLC. The life cycle aims to support all members of data mining project teams as well as IT managers and academic researchers and may improve project success rates and strategic decision support. An extensive analysis of eight life cycles leads to a list of advantages, disadvantages, and characteristics of the life cycles. This is extended and generates a conglomerate of several guidelines which serve as the foundation for the development of a new generic data mining

An Enhanced Data Mining Life Cycle

Data mining projects are complex and can have a high failure rate. In order to improve project management and success rates of such projects a life cycle is vital to the overall success of the project. This paper reports on a research project that was concerned with the life cycle development for data mining projects, its team members and their role. The paper provides a detailed view of the design and development of the data mining life cycle called DMLC. The life cycle aims to support all members of data mining project teams as well as IT managers and academic researchers and may improve project success rates and strategic decision support. An extensive analysis of eight life cycles leads to a list of advantages, disadvantages, and characteristics of the life cycles. This is extended and generates a conglomerate of several guidelines which serve as the foundation for the development of a new generic data mining life cycle. A detailed study of the human resources involved in a data mining project enhances the DMLC

Traditional and Alternative Risk: An Application to Hedge Fund Returns

We analyse the evolution of the hedge fund industry and try to assess whether this alternative investment class makes sense over the traditional one. We are concerned with the impact of the crisis. Common sense tells us that that during phases of market euphoria, possibly due to over-optimism, investors may be attracted by potentially high returns promised by the leveraged structures and the aggressive investment policies of this class of funds. When the downturns hit, managerial opacity heightened by lack of regulations, scarce liquidity and level of risks (supposedly) higher than market portfolio can trigger severe losses in investors' portfolios. Thereupon, we tested empirically whether bear markets have a stronger impact on performances of these funds when compared with traditional investment classes and, dealing in terms of relative performances and losses, our results do not always comply with the common wisdom. Instrumental to this we introduce a specific metric for assessing hedge fund performance, comprising both the relative the advantage and risk of the alternative investment over the traditional one

Metodología propuesta para la predicción de deserción universitaria mediante explotación de información

Se ha comprobado que la aplicación de procesos de Explotación de Información en el ámbito de la educación tiene resultados sumamente positivos. Por otro lado, en nuestro país la cantidad de graduados es baja, el Sistema Universitario Argentino maximiza la cantidad de estudiantes, pero minimiza la cantidad de graduados; especialmente en las carreras científicas y tecnológicas, que son esenciales para el mundo moderno de la producción. En este contexto, el objetivo principal del presente trabajo es proponer una metodología a través de la cual las Universidades puedan identificar aquellos factores que son determinantes en la deserción universitaria dentro del ámbito de las carreras ingenieriles.Eje: Tecnología informática aplicada en educaciónRed de Universidades con Carreras en Informática (RedUNCI

Metodología propuesta para la predicción de deserción universitaria mediante explotación de información

Se ha comprobado que la aplicación de procesos de Explotación de Información en el ámbito de la educación tiene resultados sumamente positivos. Por otro lado, en nuestro país la cantidad de graduados es baja, el Sistema Universitario Argentino maximiza la cantidad de estudiantes, pero minimiza la cantidad de graduados; especialmente en las carreras científicas y tecnológicas, que son esenciales para el mundo moderno de la producción. En este contexto, el objetivo principal del presente trabajo es proponer una metodología a través de la cual las Universidades puedan identificar aquellos factores que son determinantes en la deserción universitaria dentro del ámbito de las carreras ingenieriles.Eje: Tecnología informática aplicada en educaciónRed de Universidades con Carreras en Informática (RedUNCI

Metodología propuesta para la predicción de deserción universitaria mediante explotación de información

Se ha comprobado que la aplicación de procesos de Explotación de Información en el ámbito de la educación tiene resultados sumamente positivos. Por otro lado, en nuestro país la cantidad de graduados es baja, el Sistema Universitario Argentino maximiza la cantidad de estudiantes, pero minimiza la cantidad de graduados; especialmente en las carreras científicas y tecnológicas, que son esenciales para el mundo moderno de la producción. En este contexto, el objetivo principal del presente trabajo es proponer una metodología a través de la cual las Universidades puedan identificar aquellos factores que son determinantes en la deserción universitaria dentro del ámbito de las carreras ingenieriles.Eje: Tecnología informática aplicada en educaciónRed de Universidades con Carreras en Informática (RedUNCI

Ingeniería de proyectos de explotación de información para PyMEs

Los proyectos de explotación de información poseen características muy distintas a las de los proyectos de desarrollo de software tradicionales. Las clásicas etapas de análisis, diseño, desarrollo, integración y testeo, no encajan con las etapas naturales de los procesos de desarrollo de este tipo de proyectos. En consecuencia, herramientas de la Ingeniería de Software clásica tales como la ingeniería de requerimientos, los modelos de procesos, los ciclos de vida e incluso los mapas de actividades no son aplicables a este tipo de proyectos. En este contexto, este proyecto busca desarrollar y sistematizar el cuerpo de conocimiento de la Ingeniería de Proyectos de Explotación de Información con focalización en su transferencia a la Industria, particularmente al sector PyMEs. Utilizando las metodologías de investigación documental exploratoria, prototipado evolutivo y casos de estudio se plantea a través de objetivos específicos, el desarrollo de los siguientes artefactos de Ingeniería de Proyectos de Explotación de Información: [a] una batería de técnicas de educción y formalismos de documentación de requerimientos; [b] un modelo de procesos y las métricas asociadas; [c] un modelo de ciclo de vida; y [d] un mapa de actividades.Eje: Base de datos y Minería de datosRed de Universidades con Carreras en Informática (RedUNCI

The 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies

This publication comprises the papers presented at the 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland, on May 9-11, 1995. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed

Proceedings of the 4th Workshop of the MPM4CPS COST Action

Proceedings of the 4th Workshop of the MPM4CPS COST Action with the presentations delivered during the workshop and papers with extended versions of some of them

Automatic Generation of Trace Links in Model-driven Software Development

Traceability data provides the knowledge on dependencies and logical relations existing amongst artefacts that are created during software development. In reasoning over traceability data, conclusions can be drawn to increase the quality of software. The paradigm of Model-driven Software Engineering (MDSD) promotes the generation of software out of models. The latter are specified through different modelling languages. In subsequent model transformations, these models are used to generate programming code automatically. Traceability data of the involved artefacts in a MDSD process can be used to increase the software quality in providing the necessary knowledge as described above. Existing traceability solutions in MDSD are based on the integral model mapping of transformation execution to generate traceability data. Yet, these solutions still entail a wide range of open challenges. One challenge is that the collected traceability data does not adhere to a unified formal definition, which leads to poorly integrated traceability data. This aggravates the reasoning over traceability data. Furthermore, these traceability solutions all depend on the existence of a transformation engine. However, not in all cases pertaining to MDSD can a transformation engine be accessed, while taking into account proprietary transformation engines, or manually implemented transformations. In these cases it is not possible to instrument the transformation engine for the sake of generating traceability data, resulting in a lack of traceability data. In this work, we address these shortcomings. In doing so, we propose a generic traceability framework for augmenting arbitrary transformation approaches with a traceability mechanism. To integrate traceability data from different transformation approaches, our approach features a methodology for augmentation possibilities based on a design pattern. The design pattern supplies the engineer with recommendations for designing the traceability mechanism and for modelling traceability data. Additionally, to provide a traceability mechanism for inaccessible transformation engines, we leverage parallel model matching to generate traceability data for arbitrary source and target models. This approach is based on a language-agnostic concept of three similarity measures for matching. To realise the similarity measures, we exploit metamodel matching techniques for graph-based model matching. Finally, we evaluate our approach according to a set of transformations from an SAP business application and the domain of MDSD