O mapeamento de áreas com predisposição à ocorrência de eventos adversos, que resultam em ameaça e danos a sociedade, é uma demanda de elevada importância, principalmente pelo papel que exerce em ações de planejamento, gestão ambiental, territorial e de riscos. Diante disso, este trabalho busca contribuir na qualificação de metodologias e parâmetros morfométricos para mapeamento de suscetibilidade a movimentos de massa através de SIG e Sensoriamento Remoto, um dos objetivos é aplicar e comparar metodologias de suscetibilidade a movimentos de massa, entre elas o Shalstab, e a Árvore de Decisão que ainda é pouco utilizada nessa área. Buscando um consenso acerca da literatura, fez-se necessário organizar as informações referentes aos eventos adversos através de classificação, para isso foram revisados os conceitos relacionados com desastres, tais como suscetibilidade, vulnerabilidade, perigo e risco. Também foi realizado um estudo no município de Três Coroas – RS, onde foram relacionadas as ocorrências de movimentos de massa e as zonas de risco da CPRM. A partir de parâmetros morfométricos, foram identificados padrões de ocorrência de deslizamentos, e a contribuição de fatores como uso, ocupação e declividade. Por fim, foram comparados dois métodos de mapeamento de suscetibilidade, o modelo Shalstab e a Árvore de Decisão. Como dado de entrada dos modelos foram utilizados parâmetros morfométricos, extraídos de imagens SRTM, e amostras de deslizamentos, identificadas por meio de imagens de satélite de alta resolução espacial. A comparação das metodologias e a análise da acurácia obteve uma resposta melhor para a Árvore de Decisão. A diferença, entretanto, foi pouco significativa e ambos podem representar de forma satisfatória o mapa de suscetibilidade. No entanto, o Shalstab apresentou mais limitações, devido à necessidade de dados de maior resolução espacial. A aplicação de metodologias utilizando SIG e Sensoriamento Remoto contribuíram com uma maior qualificação em relação à prevenção de danos ocasionados por movimentos de massa. Ressalta-se, entretanto, a necessidade de inventários consistentes, para obter uma maior confiabilidade na aplicação dos modelos. / The mapping of areas with predisposition to adverse events, which result in threat and damage to society, is a demand of great importance, mainly for the role it plays in planning, environmental, territorial and risk management actions. Therefore, this work seeks to contribute to the qualification of methodologies and morphometric parameters for mapping susceptibility to mass movements through GIS and Remote Sensing, one of the objectives is to apply and compare methodologies of susceptibility to mass movements, among them Shalstab, and the Decision Tree that is still little used in this area. Seeking a consensus about the literature, it was necessary to organize the information regarding the adverse events through classification, for this the concepts related to disasters such as susceptibility, vulnerability, danger and risk were reviewed. A study was also carried out in the city of Três Coroas - RS, where the occurrence of mass movements and the risk zones of CPRM were related. From morphometric parameters, patterns of occurrence of landslides were identified, and the contribution of factors such as use, occupation and declivity. Finally, two methods of susceptibility mapping, the Shalstab model and the Decision Tree, were compared. Morphometric parameters, extracted from SRTM images, and sliding samples, identified by means of high spatial resolution satellite images, were used as input data. The comparison of the methodologies and the analysis of the accuracy obtained a better answer for the Decision Tree. The difference, however, was insignificant and both can represent satisfactorily the map of susceptibility. However, Shalstab presented more limitations due to the need for higher spatial resolution data. The application of methodologies using GIS and Remote Sensing contributed with a higher qualification in relation to the prevention of damages caused by mass movements. However, the need for consistent inventories to obtain greater reliability in the application of the models is emphasized.

En la actualidad, las empresas manejan una gran cantidad de información, el cual era inimaginable años atrás, la capacidad de recolectarla es muy impresionante. En consecuencia, para varias empresas esta información se ha convertido en un tema difícil de manejar. Diariamente, las empresas sea del sector, tipo o tamaño que sea, toman decisiones, las cuales la mayoría son decisiones estratégicas que pueden afectar el correcto funcionamiento de la empresa. Es aquí, donde ingresa una de las herramientas más mencionadas en el área de TI: Business Intelligence, este término se refiere al uso de datos en una empresa para facilitar la toma de decisiones, explotar su información, y mejor aún, plantear o predecir escenarios a futuro. El presente trabajo permitirá al área de Marketing de la empresa Iasacorp International, obtener información sobre el comportamiento y hábitos de compra de los clientes, mediante técnicas de minería de datos como Árbol de Decisión y técnicas de análisis predictivo, la cual ayudará a la toma de decisiones para establecer estrategias de venta de las líneas (bisutería, complementos de vestir, accesorios de cabello, etc.) que maneja la empresa y de las próximas compras. De acuerdo a lo planteado anterior mente, la implementación de este tipo de sistemas de información ofrece a la empresa ventajas competitivas, permite a la gerencia analizar y entender mejor la información y por consecuencia tomar mejores decisiones de negocio. At present, companies handle a lot of information, which was unimaginable years ago, the ability to collect it is very impressive. Consequently, for many companies this information has become a difficult issue to handle. Due to the large volume of information we have, instead of being useful you can fall in a failed attempt to give proper use. Every day, companies in any sector, type or size, make decisions, most of which are strategic decisions that may affect the proper functioning of the company. It´s here, where we talk about the most mentioned tools in the area of IT: Business Intelligence, this term refers to the use of data in an enterprise to facilitate decision-making, exploit their information, and better yet, raise or predict scenarios future. This work will allow the area Iasacorp Marketing Company International, information on the behavior and buying habits of customers, through predictive analysis techniques, which will help the decision to establish sales strategies lines (jewelry, clothing, hair accessories, etc.) that manages the company and nearby shopping. According to the points made above, the implementation of such information systems offers companies competitive advantages, allows management to better analyze and understand information and consequently make better business decisions.

A fixação das duplas diferenças de ambigüidades no processamento dos dados da fase da portadora do Sistema de Posicionamento Global (GPS), é um dos pontos cruciais no posicionamento relativo estático. Esta fixação também é utilizada como um indicador de qualidade e fornece maior segurança quanto ao resultado do posicionamento. No entanto, ela é uma informação puramente estatística baseada na precisão da medida e dissociada da exatidão das coordenadas geradas na solução. A informação sobre a exatidão das coordenadas de pontos medidos através de um vetor simples, é sempre inacessível, independente de a solução ser fixa ou “float”. Além disso, existe um risco maior em assumir um resultado de solução “float”, mesmo que ele tenha uma boa, porém, desconhecida exatidão. Por estes motivos a solução “float” não é aceita por muitos contratantes de serviços GPS, feitos com a fase da portadora, que exigem uma nova coleta de dados, com o conseqüente dispêndio de tempo e dinheiro. Essa tese foi desenvolvida no sentido de encontrar um procedimento que melhore esta situação. Para tanto, se investigou o comportamento da exatidão em medidas obtidas com a fase da portadora L1 do GPS, monitorando os fatores variáveis presentes neste tipo de medição, o que tornou possível a classificação da exatidão de resultados. Inicialmente, a partir de um conjunto de dados GPS, coletados ao longo dos anos de 2003, 2004 e 2005 em duas bases de monitoramento contínuo da USP, se fez uma análise sistemática do comportamento das variáveis contidas nos dados. A seguir se estruturou um banco de dados, que foi usado como referência na indução de uma árvore de decisão adotada como paradigma. Por último, a partir desta árvore se pôde inferir a exatidão de soluções de posicionamento obtidas com o uso da portadora L1. A validação do procedimento foi feita através da classificação da exatidão de resultados de várias linhas base, coletadas em diferentes condições e locais do estado de São Paulo e do Brasil / The most crucial step on the relative static positioning, when using the Global Positioning System (GPS) carrier phase data, is the fixing ambiguities integer values. The integer ambiguity solution is also used as a quality indicator, ensuring quality to the positioning results. In despite of its capability, the ambiguity fix solution is purely statistical information, based on the precision of measurements and completely apart from the coordinate's solution accuracy. In a single baseline processing, the positioning coordinates accuracy is always inaccessible, no matter if the final solution is float or fixed. In fact, there is some inner risk when using the float solution, although they have a good, nevertheless, unknown accuracy. Probably that is why several GPS job contractors reject the float solutions and require a new data observation, with the consequent time and money loss. This research was developed to improve that situation, investigation the inner accuracy in several GPS L1 carrier phase measurements. Checking the variable factors existing on this kind of measurement it was possible to classify the results accuracy behavior. The investigation was developed in tree steps: started with the systematic analysis of a group of L1 observation data, collected during the years: 2003, 2004 and 2005, followed by the construction of a structured data bank which generated a decision tree, performing the paradigm used to classify the accuracy of any measurement made with GPS L1 carrier phase; and ended with the research validation, through the accuracy classification that was made on several baselines, collected on different conditions and places around the state of São Paulo and Brazil

Decision trees are recognized and widely used technique for processing and analyzing data. These trees are designed with typical and generally known inductive techniques (such as ID3, C4.5, C5.0, CART, CHAID, MARS). Predictive power of created trees is not always perfect and they often provide a room for improvement. Induction of trees with difficult criterias is hard and sometime impossible. In this paper we will deal with decision trees, namely their creation. We use the mentioned room for improvement by metaheuristic, genetic algorithms, which is used in all types of optimalization. The work also includes an implementation of a new proposed algorithm in the form of plug-in into Weka environment. A comparison of the proposed method for induction of decision trees with known algorithm C4.5 is an integral part of this thesis. Powered by TCPDF (www.tcpdf.org)

This thesis describes Category management from the theoretical as well as from the practical point of view. It solves a concrete project between supplier (Danone) and customer (Jednota České Budějovice) on traditional market using all tools conected with Category management in terms of assortment analysis and shelf layouts.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / There are currently several types of databases that have different ways of manipulating data that affects the performance of transactions when dealing with the information stored. And it is very important for companies to manage information fast, so they do not lose any operation because of a bad performance of a database, in the same way, they need to operate fast while keeping the integrity of the information. Likewise, every database category's purpose is to serve a specific or specifics use cases to perform fast to manage the information when needed, so in this paper, we study and analyze the SQL, NoSQL and In Memory databases to understand their fit uses cases and make performance tests to build a decision tree that can help to take the decision to choose what database category to use to maintain a good performance. The precision of the tests of relational databases was 96.26% in NoSQL databases was 91.83% and finally in IMDBS was 93.87%.

Appropriate access management techniques can improve the safety and efficiency of arterial roads. In order to determine which roads can most benefit by the implementation of access management techniques, a prioritization process was developed to recommend various access management treatments such as limiting access points, installing raised medians, and ensuring adequate signal spacing along corridors. To serve as the basis for the performance index, a database was created including identifying features, characteristics, and crash history for 175 arterial road segments on Utah state routes. Stepwise linear regression was applied to the data collected to determine which characteristics of the roads were correlated with crash rate, crash severity, and specific collision types. Signal spacing, access density, and median type were all determined to be correlated with crash rates and crash severity. Specifically, signals per mile, access density, and two-way left-turn lanes were all positively correlated with crashes. Other characteristics such as adjacent land use and volume were also analyzed. Finally, recommendations for access management treatments were given in the form of a decision tree. The decision tree may be used to classify existing or future road segments into subcategories based on volume, signal spacing, land use, and other criteria, with recommendations provided for each subcategory.

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Yes / Enterprises and individual users heavily rely on the abilities of antiviruses and other security mechanisms. However, the methodologies used by such software are not enough to detect and prevent most of the malicious activities and also consume a huge amount of resources of the host machine for their regular oper- ations. In this paper, we propose a combination of machine learning techniques applied on a rich set of features extracted from a large dataset of benign and malicious les through a bespoke feature extraction tool. We extracted a rich set of features from each le and applied support vector machine, decision tree, and boosting on decision tree to get the highest possible detection rate. We also introduce a cloud-based scalable architecture hosted on Amazon web services to cater the needs of detection methodology. We tested our methodology against di erent scenarios and generated high achieving results with lowest energy con- sumption of the host machine.

Yes / Social media provides a significant avenue for stakeholder engagement which is crucial to ensure loyalty and satisfaction of stakeholders who possess valuable resources that can influence the business outcomes. Container lines – imperative members of global supply chains and facilitators of international trade – utilize social media to engage their stakeholders due to environmental and commercial complexity of their business. However, not all social media posts generate the same amount of stakeholder engagement. This study aims to identify and examine the social media post characteristics that lead to higher stakeholder engagement in the container shipping market. The study applies Chi-Squared Automatic Interaction Detection method to categorize social media posts based on their engagement levels. The analysis is conducted on the tweets of four global container lines which are posted between 1 September 2018 and 31 January 2019. The results demonstrate that social media posts of container lines have varying effects on engagement level. We found that fluency of tweets, tangibility of company resources in the tweet, vividness level, content type, existence of a link, and existence of a call-to-action significantly influence the container lines’ stakeholder engagement rate. This study is the first that finds out social media post classes based on the interaction between their characteristics and engagement rates by employing a decision tree methodology. The results are expected to help container lines in their social media management and stakeholder engagement policies.