Experimentos de mineração de dados aplicados a sistemas scada de usinas hidrelétricas

O atual modelo do setor elétrico brasileiro permite igualdade de condições a todos os agentes e reduz o papel do Estado no setor. Esse modelo obriga as empresas do setor a melhorarem cada vez mais a qualidade de seu produto e, como requisito para este objetivo, devem fazer uso mais efetivo da enorme quantidade de dados operacionais que são armazenados em bancos de dados, provenientes da operação dos seus sistemas elétricos e que tem nas Usinas Hidrelétricas (UHE) a sua principal fonte de geração de energia. Uma das principais ferramentas para gerenciamento dessas usinas são os sistemas de Supervisão, Controle e Aquisição de Dados (Supervisory Control And Data Acquisition - SCADA). Assim, a imensa quantidade de dados acumulados nos bancos de dados pelos sistemas SCADA, muito provavelmente contendo informações relevantes, deve ser tratada para descobrir relações e padrões e assim ajudar na compreensão de muitos aspectos operacionais importantes e avaliar o desempenho dos sistemas elétricos de potência. O processo de Descoberta de Conhecimento em Banco de Dados (Knowledge Discovery in Database - KDD) é o processo de identificar, em grandes conjuntos de dados, padrões que sejam válidos, novos, úteis e compreensíveis, para melhorar o entendimento de um problema ou um procedimento de tomada de decisão. A Mineração de Dados (ou Data Mining) é o passo dentro do KDD que permite extrair informações úteis em grandes bases de dados. Neste cenário, o presente trabalho se propõe a realizar experimentos de mineração de dados nos dados gerados por sistemas SCADA em UHE, a fim de produzir informações relevantes para auxiliar no planejamento, operação, manutenção e segurança das hidrelétricas e na implantação da cultura da mineração de dados aplicada a estas usinas.

Identificação e estimação de ruído em redes DSL: uma abordagem baseada em inteligência computacional

Este trabalho propõe a utilização de técnicas de inteligência computacional objetivando identificar e estimar a potencia de ruídos em redes Digital Subscriber Line ou Linhas do Assinante Digital (DSL) em tempo real. Uma metodologia baseada no Knowledge Discovery in Databases ou Descobrimento de Conhecimento em Bases de Dados (KDD) para detecção e estimação de ruídos em tempo real, foi utilizada. KDD é aplicado para selecionar, pré-processar e transformar os dados antes da etapa de aplicação dos algoritmos na etapa de mineração de dados. Para identificação dos ruídos o algoritmo tradicional backpropagation baseado em Redes Neurais Artificiais (RNA) é aplicado objetivando identificar o tipo de ruído em predominância durante a coleta das informações do modem do usuário e da central. Enquanto, para estimação o algoritmo de regressão linear e o algoritmo híbrido composto por Fuzzy e regressão linear foram aplicados para estimar a potência em Watts de ruído crosstalk ou diafonia na rede. Os resultados alcançados demonstram que a utilização de algoritmos de inteligência computacional como a RNA são promissores para identificação de ruídos em redes DSL, e que algoritmos como de regressão linear e Fuzzy com regressão linear (FRL) são promissores para a estimação de ruídos em redes DSL.

Imputação de dados baseado em otimização por enxame de partículas considerando os principais mecanismos de ausência de dados

Durante o processo de extração do conhecimento em bases de dados, alguns problemas podem ser encontrados como por exemplo, a ausência de determinada instância de um atributo. A ocorrência de tal problemática pode causar efeitos danosos nos resultados finais do processo, pois afeta diretamente a qualidade dos dados a ser submetido a um algoritmo de aprendizado de máquina. Na literatura, diversas propostas são apresentadas a fim de contornar tal dano, dentre eles está a de imputação de dados, a qual estima um valor plausível para substituir o ausente. Seguindo essa área de solução para o problema de valores ausentes, diversos trabalhos foram analisados e algumas observações foram realizadas como, a pouca utilização de bases sintéticas que simulem os principais mecanismos de ausência de dados e uma recente tendência a utilização de algoritmos bio-inspirados como tratamento do problema. Com base nesse cenário, esta dissertação apresenta um método de imputação de dados baseado em otimização por enxame de partículas, pouco explorado na área, e o aplica para o tratamento de bases sinteticamente geradas, as quais consideram os principais mecanismos de ausência de dados, MAR, MCAR e NMAR. Os resultados obtidos ao comprar diferentes configurações do método à outros dois conhecidos na área (KNNImpute e SVMImpute) são promissores para sua utilização na área de tratamento de valores ausentes uma vez que alcançou os melhores valores na maioria dos experimentos realizados.

Otimização do processo de aprendizagem da estrutura gráfica de Redes Bayesianas em BigData

A automação na gestão e análise de dados tem sido um fator crucial para as empresas que necessitam de soluções eficientes em um mundo corporativo cada vez mais competitivo. A explosão do volume de informações, que vem se mantendo crescente nos últimos anos, tem exigido cada vez mais empenho em buscar estratégias para gerenciar e, principalmente, extrair informações estratégicas valiosas a partir do uso de algoritmos de Mineração de Dados, que comumente necessitam realizar buscas exaustivas na base de dados a fim de obter estatísticas que solucionem ou otimizem os parâmetros do modelo de extração do conhecimento utilizado; processo que requer computação intensiva para a execução de cálculos e acesso frequente à base de dados. Dada a eficiência no tratamento de incerteza, Redes Bayesianas têm sido amplamente utilizadas neste processo, entretanto, à medida que o volume de dados (registros e/ou atributos) aumenta, torna-se ainda mais custoso e demorado extrair informações relevantes em uma base de conhecimento. O foco deste trabalho é propor uma nova abordagem para otimização do aprendizado da estrutura da Rede Bayesiana no contexto de BigData, por meio do uso do processo de MapReduce, com vista na melhora do tempo de processamento. Para tanto, foi gerada uma nova metodologia que inclui a criação de uma Base de Dados Intermediária contendo todas as probabilidades necessárias para a realização dos cálculos da estrutura da rede. Por meio das análises apresentadas neste estudo, mostra-se que a combinação da metodologia proposta com o processo de MapReduce é uma boa alternativa para resolver o problema de escalabilidade nas etapas de busca em frequência do algoritmo K2 e, consequentemente, reduzir o tempo de resposta na geração da rede.

Collaborative fuzzy clustering algorithms: some refinements and design guidelines

There are some variants of the widely used Fuzzy C-Means (FCM) algorithm that support clustering data distributed across different sites. Those methods have been studied under different names, like collaborative and parallel fuzzy clustering. In this study, we offer some augmentation of the two FCM-based clustering algorithms used to cluster distributed data by arriving at some constructive ways of determining essential parameters of the algorithms (including the number of clusters) and forming a set of systematically structured guidelines such as a selection of the specific algorithm depending on the nature of the data environment and the assumptions being made about the number of clusters. A thorough complexity analysis, including space, time, and communication aspects, is reported. A series of detailed numeric experiments is used to illustrate the main ideas discussed in the study.

Seeing beyond reading: a survey on visual text analytics

We review recent visualization techniques aimed at supporting tasks that require the analysis of text documents, from approaches targeted at visually summarizing the relevant content of a single document to those aimed at assisting exploratory investigation of whole collections of documents.Techniques are organized considering their target input materialeither single texts or collections of textsand their focus, which may be at displaying content, emphasizing relevant relationships, highlighting the temporal evolution of a document or collection, or helping users to handle results from a query posed to a search engine.We describe the approaches adopted by distinct techniques and briefly review the strategies they employ to obtain meaningful text models, discuss how they extract the information required to produce representative visualizations, the tasks they intend to support and the interaction issues involved, and strengths and limitations. Finally, we show a summary of techniques, highlighting their goals and distinguishing characteristics. We also briefly discuss some open problems and research directions in the fields of visual text mining and text analytics.

CID: an efficient complexity-invariant distance for time series

The ubiquity of time series data across almost all human endeavors has produced a great interest in time series data mining in the last decade. While dozens of classification algorithms have been applied to time series, recent empirical evidence strongly suggests that simple nearest neighbor classification is exceptionally difficult to beat. The choice of distance measure used by the nearest neighbor algorithm is important, and depends on the invariances required by the domain. For example, motion capture data typically requires invariance to warping, and cardiology data requires invariance to the baseline (the mean value). Similarly, recent work suggests that for time series clustering, the choice of clustering algorithm is much less important than the choice of distance measure used.In this work we make a somewhat surprising claim. There is an invariance that the community seems to have missed, complexity invariance. Intuitively, the problem is that in many domains the different classes may have different complexities, and pairs of complex objects, even those which subjectively may seem very similar to the human eye, tend to be further apart under current distance measures than pairs of simple objects. This fact introduces errors in nearest neighbor classification, where some complex objects may be incorrectly assigned to a simpler class. Similarly, for clustering this effect can introduce errors by “suggesting” to the clustering algorithm that subjectively similar, but complex objects belong in a sparser and larger diameter cluster than is truly warranted.We introduce the first complexity-invariant distance measure for time series, and show that it generally produces significant improvements in classification and clustering accuracy. We further show that this improvement does not compromise efficiency, since we can lower bound the measure and use a modification of triangular inequality, thus making use of most existing indexing and data mining algorithms. We evaluate our ideas with the largest and most comprehensive set of time series mining experiments ever attempted in a single work, and show that complexity-invariant distance measures can produce improvements in classification and clustering in the vast majority of cases.

Body ares networks for human motor assessment

Healthcare, Human Computer Interfaces (HCI), Security and Biometry are the most promising application scenario directly involved in the Body Area Networks (BANs) evolution. Both wearable devices and sensors directly integrated in garments envision a word in which each of us is supervised by an invisible assistant monitoring our health and daily-life activities. New opportunities are enabled because improvements in sensors miniaturization and transmission efficiency of the wireless protocols, that achieved the integration of high computational power aboard independent, energy-autonomous, small form factor devices. Application’s purposes are various: (I) data collection to achieve off-line knowledge discovery; (II) user notification of his/her activities or in case a danger occurs; (III) biofeedback rehabilitation; (IV) remote alarm activation in case the subject need assistance; (V) introduction of a more natural interaction with the surrounding computerized environment; (VI) users identification by physiological or behavioral characteristics. Telemedicine and mHealth [1] are two of the leading concepts directly related to healthcare. The capability to borne unobtrusiveness objects supports users’ autonomy. A new sense of freedom is shown to the user, not only supported by a psychological help but a real safety improvement. Furthermore, medical community aims the introduction of new devices to innovate patient treatments. In particular, the extension of the ambulatory analysis in the real life scenario by proving continuous acquisition. The wide diffusion of emerging wellness portable equipment extended the usability of wearable devices also for fitness and training by monitoring user performance on the working task. The learning of the right execution techniques related to work, sport, music can be supported by an electronic trainer furnishing the adequate aid. HCIs made real the concept of Ubiquitous, Pervasive Computing and Calm Technology introduced in the 1988 by Marc Weiser and John Seeley Brown. They promotes the creation of pervasive environments, enhancing the human experience. Context aware, adaptive and proactive environments serve and help people by becoming sensitive and reactive to their presence, since electronics is ubiquitous and deployed everywhere. In this thesis we pay attention to the integration of all the aspects involved in a BAN development. Starting from the choice of sensors we design the node, configure the radio network, implement real-time data analysis and provide a feedback to the user. We present algorithms to be implemented in wearable assistant for posture and gait analysis and to provide assistance on different walking conditions, preventing falls. Our aim, expressed by the idea to contribute at the development of a non proprietary solutions, driven us to integrate commercial and standard solutions in our devices. We use sensors available on the market and avoided to design specialized sensors in ASIC technologies. We employ standard radio protocol and open source projects when it was achieved. The specific contributions of the PhD research activities are presented and discussed in the following. • We have designed and build several wireless sensor node providing both sensing and actuator capability making the focus on the flexibility, small form factor and low power consumption. The key idea was to develop a simple and general purpose architecture for rapid analysis, prototyping and deployment of BAN solutions. Two different sensing units are integrated: kinematic (3D accelerometer and 3D gyroscopes) and kinetic (foot-floor contact pressure forces). Two kind of feedbacks were implemented: audio and vibrotactile. • Since the system built is a suitable platform for testing and measuring the features and the constraints of a sensor network (radio communication, network protocols, power consumption and autonomy), we made a comparison between Bluetooth and ZigBee performance in terms of throughput and energy efficiency. Test in the field evaluate the usability in the fall detection scenario. • To prove the flexibility of the architecture designed, we have implemented a wearable system for human posture rehabilitation. The application was developed in conjunction with biomedical engineers who provided the audio-algorithms to furnish a biofeedback to the user about his/her stability. • We explored off-line gait analysis of collected data, developing an algorithm to detect foot inclination in the sagittal plane, during walk. • In collaboration with the Wearable Lab – ETH, Zurich, we developed an algorithm to monitor the user during several walking condition where the user carry a load. The remainder of the thesis is organized as follows. Chapter I gives an overview about Body Area Networks (BANs), illustrating the relevant features of this technology and the key challenges still open. It concludes with a short list of the real solutions and prototypes proposed by academic research and manufacturers. The domain of the posture and gait analysis, the methodologies, and the technologies used to provide real-time feedback on detected events, are illustrated in Chapter II. The Chapter III and IV, respectively, shown BANs developed with the purpose to detect fall and monitor the gait taking advantage by two inertial measurement unit and baropodometric insoles. Chapter V reports an audio-biofeedback system to improve balance on the information provided by the use centre of mass. A walking assistant based on the KNN classifier to detect walking alteration on load carriage, is described in Chapter VI.

IDENTIFICATION OF GENES CONTROLLING BIOLOGICAL PROCESSES AND PATHWAYS THROUGH STATISTICAL ANALYSIS AND NETWORK RECONSTRUCTION

The developmental processes and functions of an organism are controlled by the genes and the proteins that are derived from these genes. The identification of key genes and the reconstruction of gene networks can provide a model to help us understand the regulatory mechanisms for the initiation and progression of biological processes or functional abnormalities (e.g. diseases) in living organisms. In this dissertation, I have developed statistical methods to identify the genes and transcription factors (TFs) involved in biological processes, constructed their regulatory networks, and also evaluated some existing association methods to find robust methods for coexpression analyses. Two kinds of data sets were used for this work: genotype data and gene expression microarray data. On the basis of these data sets, this dissertation has two major parts, together forming six chapters. The first part deals with developing association methods for rare variants using genotype data (chapter 4 and 5). The second part deals with developing and/or evaluating statistical methods to identify genes and TFs involved in biological processes, and construction of their regulatory networks using gene expression data (chapter 2, 3, and 6). For the first part, I have developed two methods to find the groupwise association of rare variants with given diseases or traits. The first method is based on kernel machine learning and can be applied to both quantitative as well as qualitative traits. Simulation results showed that the proposed method has improved power over the existing weighted sum method (WS) in most settings. The second method uses multiple phenotypes to select a few top significant genes. It then finds the association of each gene with each phenotype while controlling the population stratification by adjusting the data for ancestry using principal components. This method was applied to GAW 17 data and was able to find several disease risk genes. For the second part, I have worked on three problems. First problem involved evaluation of eight gene association methods. A very comprehensive comparison of these methods with further analysis clearly demonstrates the distinct and common performance of these eight gene association methods. For the second problem, an algorithm named the bottom-up graphical Gaussian model was developed to identify the TFs that regulate pathway genes and reconstruct their hierarchical regulatory networks. This algorithm has produced very significant results and it is the first report to produce such hierarchical networks for these pathways. The third problem dealt with developing another algorithm called the top-down graphical Gaussian model that identifies the network governed by a specific TF. The network produced by the algorithm is proven to be of very high accuracy.

Combining data mining and ontology engineering to enrich ontologies and linked data

In this position paper, we claim that the need for time consuming data preparation and result interpretation tasks in knowledge discovery, as well as for costly expert consultation and consensus building activities required for ontology building can be reduced through exploiting the interplay of data mining and ontology engineering. The aim is to obtain in a semi-automatic way new knowledge from distributed data sources that can be used for inference and reasoning, as well as to guide the extraction of further knowledge from these data sources. The proposed approach is based on the creation of a novel knowledge discovery method relying on the combination, through an iterative ?feedbackloop?, of (a) data mining techniques to make emerge implicit models from data and (b) pattern-based ontology engineering to capture these models in reusable, conceptual and inferable artefacts.

1RM prediction: a data mining case study

In the last few years there has been a heightened interest in data treatment and analysis with the aim of discovering hidden knowledge and eliciting relationships and patterns within this data. Data mining techniques (also known as Knowledge Discovery in Databases) have been applied over a wide range of fields such as marketing, investment, fraud detection, manufacturing, telecommunications and health. In this study, well-known data mining techniques such as artificial neural networks (ANN), genetic programming (GP), forward selection linear regression (LR) and k-means clustering techniques, are proposed to the health and sports community in order to aid with resistance training prescription. Appropriate resistance training prescription is effective for developing fitness, health and for enhancing general quality of life. Resistance exercise intensity is commonly prescribed as a percent of the one repetition maximum. 1RM, dynamic muscular strength, one repetition maximum or one execution maximum, is operationally defined as the heaviest load that can be moved over a specific range of motion, one time and with correct performance. The safety of the 1RM assessment has been questioned as such an enormous effort may lead to muscular injury. Prediction equations could help to tackle the problem of predicting the 1RM from submaximal loads, in order to avoid or at least, reduce the associated risks. We built different models from data on 30 men who performed up to 5 sets to exhaustion at different percentages of the 1RM in the bench press action, until reaching their actual 1RM. Also, a comparison of different existing prediction equations is carried out. The LR model seems to outperform the ANN and GP models for the 1RM prediction in the range between 1 and 10 repetitions. At 75% of the 1RM some subjects (n = 5) could perform 13 repetitions with proper technique in the bench press action, whilst other subjects (n = 20) performed statistically significant (p < 0:05) more repetitions at 70% than at 75% of their actual 1RM in the bench press action. Rate of perceived exertion (RPE) seems not to be a good predictor for 1RM when all the sets are performed until exhaustion, as no significant differences (p < 0:05) were found in the RPE at 75%, 80% and 90% of the 1RM. Also, years of experience and weekly hours of strength training are better correlated to 1RM (p < 0:05) than body weight. O'Connor et al. 1RM prediction equation seems to arise from the data gathered and seems to be the most accurate 1RM prediction equation from those proposed in literature and used in this study. Epley's 1RM prediction equation is reproduced by means of data simulation from 1RM literature equations. Finally, future lines of research are proposed related to the problem of the 1RM prediction by means of genetic algorithms, neural networks and clustering techniques. RESUMEN En los últimos años ha habido un creciente interés en el tratamiento y análisis de datos con el propósito de descubrir relaciones, patrones y conocimiento oculto en los mismos. Las técnicas de data mining (también llamadas de \Descubrimiento de conocimiento en bases de datos\) se han aplicado consistentemente a lo gran de un gran espectro de áreas como el marketing, inversiones, detección de fraude, producción industrial, telecomunicaciones y salud. En este estudio, técnicas bien conocidas de data mining como las redes neuronales artificiales (ANN), programación genética (GP), regresión lineal con selección hacia adelante (LR) y la técnica de clustering k-means, se proponen a la comunidad del deporte y la salud con el objetivo de ayudar con la prescripción del entrenamiento de fuerza. Una apropiada prescripción de entrenamiento de fuerza es efectiva no solo para mejorar el estado de forma general, sino para mejorar la salud e incrementar la calidad de vida. La intensidad en un ejercicio de fuerza se prescribe generalmente como un porcentaje de la repetición máxima. 1RM, fuerza muscular dinámica, una repetición máxima o una ejecución máxima, se define operacionalmente como la carga máxima que puede ser movida en un rango de movimiento específico, una vez y con una técnica correcta. La seguridad de las pruebas de 1RM ha sido cuestionada debido a que el gran esfuerzo requerido para llevarlas a cabo puede derivar en serias lesiones musculares. Las ecuaciones predictivas pueden ayudar a atajar el problema de la predicción de la 1RM con cargas sub-máximas y son empleadas con el propósito de eliminar o al menos, reducir los riesgos asociados. En este estudio, se construyeron distintos modelos a partir de los datos recogidos de 30 hombres que realizaron hasta 5 series al fallo en el ejercicio press de banca a distintos porcentajes de la 1RM, hasta llegar a su 1RM real. También se muestra una comparación de algunas de las distintas ecuaciones de predicción propuestas con anterioridad. El modelo LR parece superar a los modelos ANN y GP para la predicción de la 1RM entre 1 y 10 repeticiones. Al 75% de la 1RM algunos sujetos (n = 5) pudieron realizar 13 repeticiones con una técnica apropiada en el ejercicio press de banca, mientras que otros (n = 20) realizaron significativamente (p < 0:05) más repeticiones al 70% que al 75% de su 1RM en el press de banca. El ínndice de esfuerzo percibido (RPE) parece no ser un buen predictor del 1RM cuando todas las series se realizan al fallo, puesto que no existen diferencias signifiativas (p < 0:05) en el RPE al 75%, 80% y el 90% de la 1RM. Además, los años de experiencia y las horas semanales dedicadas al entrenamiento de fuerza están más correlacionadas con la 1RM (p < 0:05) que el peso corporal. La ecuación de O'Connor et al. parece surgir de los datos recogidos y parece ser la ecuación de predicción de 1RM más precisa de aquellas propuestas en la literatura y empleadas en este estudio. La ecuación de predicción de la 1RM de Epley es reproducida mediante simulación de datos a partir de algunas ecuaciones de predicción de la 1RM propuestas con anterioridad. Finalmente, se proponen futuras líneas de investigación relacionadas con el problema de la predicción de la 1RM mediante algoritmos genéticos, redes neuronales y técnicas de clustering.

Transformación de series numéricas temporales en secuencias simbólicas

El Trabajo de Fin de Grado aborda el tema del Descubrimiento de Conocimiento en series numéricas temporales, abordando el análisis de las mismas desde el punto de vista de la semántica de las series. La gran mayoría de trabajos realizados hasta la fecha en el campo del análisis de series temporales proponen el análisis numérico de los valores de la serie, lo que permite obtener buenos resultados pero no ofrece la posibilidad de formular las conclusiones de forma que se puedan justificar e interpretar los resultados obtenidos. Por ello, en este trabajo se pretende crear una aplicación que permita realizar el análisis de las series temporales desde un punto de vista cualitativo, en contraposición al tradicional método cuantitativo. De esta forma, quedarán recogidos todos los elementos relevantes de la serie temporal que puedan servir de estudio en un futuro. Para abordar el objetivo propuesto se plantea un mecanismo para extraer de la serie temporal la información que resulta de interés para su análisis. Para poder hacerlo, primero se formaliza el conjunto de comportamientos relevantes del dominio, que serán los símbolos a mostrar en la salida de la aplicación. Así, el método que se ha diseñado e implementado transformará una serie temporal numérica en una secuencia simbólica que recoge toda la semántica de la serie temporal de partida y resulta más intuitiva y fácil de interpretar. Una vez que se dispone de un mecanismo para transformar las series numéricas en secuencias simbólicas, se pueden plantear todas las tareas de análisis sobre dichas secuencias de símbolos. En este trabajo, aunque no se entra en este post-análisis de estas series, sí se plantean distintos campos en los que se puede avanzar en el futuro. Por ejemplo, se podría hacer una medida de la similitud entre dos secuencias simbólicas como punto de partida para la tarea de comparación o la creación de modelos de referencia para análisis posteriores de las series temporales. ---ABSTRACT---This Final-year Project deals with the topic of Knowledge Discovery in numerical time series, addressing time series analysis from the viewpoint of the semantics of the series. Most of the research conducted to date in the field of time series analysis recommends analysing the values of the series numerically. This provides good results but prevents the conclusions from being formulated to allow justification and interpretation of the results. Thus, the purpose of this project is to create an application that allows the analysis of time series, from a qualitative point of view rather than a quantitative one. This way, all the relevant elements of the time series will be gathered for future studies. The design of a mechanism to extract the information that is of interest from the time series is the first step towards achieving the proposed objective. To do this, all the key behaviours in the domain are set, which will be the symbols shown in the output. The designed and implemented method transforms a numerical time series into a symbolic sequence that takes in all the semantics of the original time series and is more intuitive and easier to interpret. Once a mechanism for transforming the numerical series into symbolic sequences is created, the symbolic sequences are ready for analysis. Although this project does not cover a post-analysis of these series, it proposes different fields in which research can be done in the future. For instance, comparing two different sequences to measure the similarities between them, or the creation of reference models for further analysis of time series.

Análisis de datos sobre virus Informáticos

El trabajo fin de grado que se presenta en este documento trata de “Aplicar técnicas de Data Mining a un conjunto de datos procedentes de ataques de virus informáticos interceptados en servidores de Internet”. La propuesta de este trabajo surgió de una Institución con el fin de extraer información de un conjunto de datos proveniente de ejecuciones de virus informáticos. Lamentablemente, debido a fuertes restricciones de privacidad por parte de esta Institución y así como al relevo de la persona responsable de éste área en dicha Institución, el Proyecto finalmente se canceló. Como consecuencia, y teniendo en cuenta el carácter didáctico de este trabajo fin de grado, el proyecto KDD (Knowledge Discovery in Databases) en sí y sus objetivos de negocio y objetivos de data mining, se han establecido conforme con la misma temática de predicción de ataques de virus que había planteado la Institución en el pasado, contando con una base de datos que ha sido recopilada de diferentes empresas anónimas. Para llevar un desarrollo estructurado de todas las fases del proceso KDD, se ha trabajado siguiendo como referencia una metodología para proyectos de Data Mining, “CRISP-DM”, cuyo estándar incluye un modelo y una guía, estructurados en seis fases. Como herramienta de Data Mining a utilizar, se ha elegido el software de libre distribución “WEKA”. Por último, cabe destacar que el proyecto ha concluido satisfactoriamente, lográndose cada una de las metas establecidas como proyecto de minería de datos.

Monitorización inteligente de redes de comunicación

El avance tecnológico de los últimos años ha aumentado la necesidad de guardar enormes cantidades de datos de forma masiva, llegando a una situación de desorden en el proceso de almacenamiento de datos, a su desactualización y a complicar su análisis. Esta situación causó un gran interés para las organizaciones en la búsqueda de un enfoque para obtener información relevante de estos grandes almacenes de datos. Surge así lo que se define como inteligencia de negocio, un conjunto de herramientas, procedimientos y estrategias para llevar a cabo la “extracción de conocimiento”, término con el que se refiere comúnmente a la extracción de información útil para la propia organización. Concretamente en este proyecto, se ha utilizado el enfoque Knowledge Discovery in Databases (KDD), que permite lograr la identificación de patrones y un manejo eficiente de las anomalías que puedan aparecer en una red de comunicaciones. Este enfoque comprende desde la selección de los datos primarios hasta su análisis final para la determinación de patrones. El núcleo de todo el enfoque KDD es la minería de datos, que contiene la tecnología necesaria para la identificación de los patrones mencionados y la extracción de conocimiento. Para ello, se utilizará la herramienta RapidMiner en su versión libre y gratuita, debido a que es más completa y de manejo más sencillo que otras herramientas como KNIME o WEKA. La gestión de una red engloba todo el proceso de despliegue y mantenimiento. Es en este procedimiento donde se recogen y monitorizan todas las anomalías ocasionadas en la red, las cuales pueden almacenarse en un repositorio. El objetivo de este proyecto es realizar un planteamiento teórico y varios experimentos que permitan identificar patrones en registros de anomalías de red. Se ha estudiado el repositorio de MAWI Lab, en el que se han almacenado anomalías diarias. Se trata de buscar indicios característicos anuales detectando patrones. Los diferentes experimentos y procedimientos de este estudio pretenden demostrar la utilidad de la inteligencia de negocio a la hora de extraer información a partir de un almacén de datos masivo, para su posterior análisis o futuros estudios. ABSTRACT. The technological progresses in the recent years required to store a big amount of information in repositories. This information is often in disorder, outdated and needs a complex analysis. This situation has caused a relevant interest in investigating methodologies to obtain important information from these huge data stores. Business intelligence was born as a set of tools, procedures and strategies to implement the "knowledge extraction". Specifically in this project, Knowledge Discovery in Databases (KDD) approach has been used. KDD is one of the most important processes of business intelligence to achieve the identification of patterns and the efficient management of the anomalies in a communications network. This approach includes all necessary stages from the selection of the raw data until the analysis to determine the patterns. The core process of the whole KDD approach is the Data Mining process, which analyzes the information needed to identify the patterns and to extract the knowledge. In this project we use the RapidMiner tool to carry out the Data Mining process, because this tool has more features and is easier to use than other tools like WEKA or KNIME. Network management includes the deployment, supervision and maintenance tasks. Network management process is where all anomalies are collected, monitored, and can be stored in a repository. The goal of this project is to construct a theoretical approach, to implement a prototype and to carry out several experiments that allow identifying patterns in some anomalies records. MAWI Lab repository has been selected to be studied, which contains daily anomalies. The different experiments show the utility of the business intelligence to extract information from big data warehouse.

Estudio de patrones de interacción entre los estudiantes y la Plataforma de Tele-Enseñanza en la UPM

Vivimos en una sociedad en la que la información ha adquirido una vital importancia. El uso de Internet y el desarrollo de nuevos sistemas de la información han generado un ferviente interés tanto de empresas como de instituciones en la búsqueda de nuevos patrones que les proporcione la clave del éxito. La Analítica de Negocio reúne un conjunto de herramientas, estrategias y técnicas orientadas a la explotación de la información con el objetivo de crear conocimiento útil dentro de un marco de trabajo y facilitar la optimización de los recursos tanto de empresas como de instituciones. El presente proyecto se enmarca en lo que se conoce como Gestión Educativa. Se aplicará una arquitectura y modelo de trabajo similar a lo que se ha venido haciendo en los últimos años en el entorno empresarial con la Inteligencia de Negocio. Con esta variante, se pretende mejorar la calidad de la enseñanza, agilizar las decisiones dentro de la institución académica, fortalecer las capacidades del cuerpo docente y en definitiva favorecer el aprendizaje del alumnado. Para lograr el objetivo se ha decidido seguir las etapas del Knowledge Discovery in Databases (KDD), una de las metodologías más conocidas dentro de la Inteligencia de Negocio, que describe el procedimiento que va desde la selección de la información y su carga en sistemas de almacenamiento, hasta la aplicación de técnicas de minería de datos para la obtención nuevo conocimiento. Los estudios se realizan a partir de la información de la activad de los usuarios dentro la plataforma de Tele-Enseñanza de la Universidad Politécnica de Madrid (Moodle). Se desarrollan trabajos de extracción y preprocesado de la base de datos en crudo y se aplican técnicas de minería de datos. En la aplicación de técnicas de minería de datos, uno de los factores más importantes a tener en cuenta es el tipo de información que se va a tratar. Por este motivo, se trabaja con la Minería de Datos Educativa, en inglés, Educational Data Mining (EDM) que consiste en la aplicación de técnicas de minería optimizadas para la información que se genera en entornos educativos. Dentro de las posibilidades que ofrece el EDM, se ha decidido centrar los estudios en lo que se conoce como analítica predictiva. El objetivo fundamental es conocer la influencia que tienen las interacciones alumno-plataforma en las calificaciones finales y descubrir nuevas reglas que describan comportamientos que faciliten al profesorado discriminar si un estudiante va a aprobar o suspender la asignatura, de tal forma que se puedan tomar medidas que mejoren su rendimiento. Toda la información tratada en el presente proyecto ha sido previamente anonimizada para evitar cualquier tipo de intromisión que atente contra la privacidad de los elementos participantes en el estudio. ABSTRACT. We live in a society dominated by data. The use of the Internet accompanied by developments in information systems has generated a sustained interest among companies and institutions to discover new patterns to succeed in their business ventures. Business Analytics (BA) combines tools, strategies and techniques focused on exploiting the available information, to optimize resources and create useful insight. The current project is framed under Educational Management. A Business Intelligence (BI) architecture and business models taught up to date will be applied with the aim to accelerate the decision-making in academic institutions, strengthen teacher´s skills and ultimately improve the quality of teaching and learning. The best way to achieve this is to follow the Knowledge Discovery in Databases (KDD), one of the best-known methodologies in B.I. This process describes data preparation, selection, and cleansing through to the application of purely Data Mining Techniques in order to incorporate prior knowledge on data sets and interpret accurate solutions from the observed results. The studies will be performed using the information extracted from the Universidad Politécnica de Madrid Learning Management System (LMS), Moodle. The stored data is based on the user-platform interaction. The raw data will be extracted and pre-processed and afterwards, Data Mining Techniques will be applied. One of the crucial factors in the application of Data Mining Techniques is the kind of information that will be processed. For this reason, a new Data Mining perspective will be taken, called Educational Data Mining (EDM). EDM consists of the application of Data Mining Techniques but optimized for the raw data generated by the educational environment. Within EDM, we have decided to drive our research on what is called Predictive Analysis. The main purpose is to understand the influence of the user-platform interactions in the final grades of students and discover new patterns that explain their behaviours. This could allow teachers to intervene ahead of a student passing or failing, in such a way an action could be taken to improve the student performance. All the information processed has been previously anonymized to avoid the invasion of privacy.