As pesquisas na área da saúde tem sido desenvolvidas com recursos governamentais obtidos junto às agências de fomento ou com recursos próprios das instituições, num esforço conjunto para a obtenção de parâmetros, em que se destacam os estudos epidemiológicos, voltados à melhoria na resolutividade dos problemas de saúde da população. A inserção da temática “Gestão em Serviços de Saúde” na área epidemiológica surge do enfoque multidisciplinar como uma forma de complementar os estudos trazendo conhecimentos oriundos da Administração para a abordagem das questões relativas à qualidade na atenção, eqüidade, identificação dos agravos de saúde, novas tecnologias e avaliação de custoefetividade das intervenções em saúde. A capacidade de auto-sustentabilidade de um laboratório de pesquisa de uma instituição pública de saúde em relação aos seus recursos financeiros e materiais e ao desenvolvimento do seu potencial humano de forma efetiva e eficaz é o foco deste trabalho que tem por objetivo desenvolver e aplicar o sistema gerencial Balanced Scorecard (BSC) no Centro de Terapia Gênica do Hospital de Clínicas de Porto Alegre. O desenvolvimento do Balanced Scorecard no Centro de Terapia Gênica foi realizado em duas etapas: a primeiro, voltada à construção do modelo conceitual através da elaboração do Mapa Estratégico e a segunda, à definição do Plano de Ação com a seleção das áreas de indicadores do laboratório. As referências usadas nessas fases foram a revisão da literatura sobre o BSC e seu desenvolvimento em outras organizações do setor público e privado, a análise exploratória dos indicadores de desempenho sugeridos pela Associação Brasileira das Instituições de Pesquisa Tecnológica (ABIPTI), o Planejamento Estratégico do Hospital de Clínicas e o levantamento dos dados do laboratório. Os resultados obtidos indicam a viabilidade da aplicação do BSC em um laboratório de pesquisa de um hospital público de ensino. Mostram, também, que a administração das atividades de pesquisa, vista sob uma perspectiva estratégica, torna a multidisciplinaridade de conhecimentos presentes em uma instituição como um fator importante para a busca das melhores práticas gerenciais que contribuam para agregar valor científico, tecnológico e econômico às atividades de pesquisa e desta forma, auxiliem a impulsionar as atividades de P&D na organização. / The researches in healthcare have been being developed with government resources or the institutions own resources, in an joint effort to obtain parameters in which the epidemiologic studies outstand , aiming at the improvement of the solution of the health problems of the population. The insertion of the issue " Administration in Healthcare Organizations " in the epidemiologic area comes from the multidisciplinary focus as a way of complementing the studies bringing knowledge originated from the Administration to the approach of issues such as the quality of the attention, access, identification of diseases, new technology and the evaluation of cost-effectiveness of the health care intervention. The capacity of self-sustainability of a research laboratory of a public institution of healthcare in relation to its financial and material resources and to the development of its human potential capital in an effective and efficient way is the focus of this work that has as objective to develop and to apply the system management called Balanced Scorecard (BSC) in the Center for Gene Therapy of the Hospital de Clinicas de Porto Alegre. The development of the Balanced Scorecard in Center for Gene Therapy was accomplished in two stages: firstly, aiming at the construction of the conceptual model through the elaboration of the Strategic Map and, secondly, to the definition of the Plan of Action with the selection of the performance measures of the laboratory. The references used in those phases were the revision of the literature on BSC and its development in other public and private organizations, the exploratory analysis of the performance measures suggested by the Brazilian Association of the Technological Research Institutions (ABIPTI), the Strategic Planning of the Hospital de Clinicas and the obtaining of the data of the laboratory. The results obtained indicate the viability of the application of BSC in a research laboratory of a public hospital. They also show that the administration of research activities, seen under a strategic perspective, turns the multidisciplinary knowledge present in an institution as an important factor for the search of the best managerial practices that contribute to join scientific, technological and economic value to the research activities and this way, help to impel the activities of P&D in the organization.

Para superar os desafios existentes na produção citrícola, relacionados ao dinamismo do mercado, às pressões fitossanitárias e à complexidade da produção, deve-se ter um elevado nível de conhecimento do processo produtivo que conduza à tomada de decisão adequada. Este conhecimento pode ser obtido pela compreensão de como os requisitos da produção, representados pela qualidade exigida, são afetados pelas variáveis do processo, representadas por características técnicas. Considerando-se que esta compreensão auxilia na manutenção e competitividade da cultura, o presente trabalho tem como objetivo identificar, sistematizar e priorizar as características técnicas para atender as exigências do processo de produção de laranjas destinadas à indústria processadora de suco. A identificação da qualidade exigida do processo e das características técnicas da produção foi realizada por meio de pesquisa bibliográfica. Identificou-se 38 itens da qualidade exigida, com base em 6 processos e 17 atividades realizadas para a produção de laranjas, e 114 características técnicas que afetam esta produção, organizadas em 7 grupos de afinidades. O método do desdobramento da função qualidade (QFD) foi utilizado para compreender a relação entre as qualidades exigidas e as características técnicas da produção, permitindo a sistematização. A aplicação do método também permitiu a priorização das características técnicas, em uma etapa denominada conversão. A priorização foi definida com base em um peso relativo calculado para cada característica na etapa de conversão, que representa a influência de cada uma sobre o processo produtivo. Do total de 114 características, 64 (56%) foram responsáveis por 80% do peso relativo total calculado. Notou-se que a influência de cada característica sobre o processo produtivo é diferente, apesar dessa relação não atender ao princípio de Pareto. A matriz de correlação, que identifica a relação de interdependência entre as características técnicas da produção, expôs ainda mais a complexidade e o caráter sistêmico da produção citrícola. / In order to overcome citriculture\'s challenges, related to market dynamics, phytosanitary pressures and production complexity, a high level of knowledge concerning the production process is necessary, leading to proper decision making. This knowledge can be achieved by comprehension on how the production requirements, represented by demanded quality, are affected by process variables, represented by technical characteristics. Considering that this comprehension helps to maintain proper condition and competitiveness of citriculture, this study aims to identify, systematize and prioritize technical characteristics to satisfy the requirements of the orange production process to attend juice industry. The identification of process quality demanded and technical measures was performed by bibliographical research. It was identified 38 items of demanded quality, based on 6 process and 17 activities performed on oranges production, and 114 technical characteristics that affect such production, organized into 7 affinity groups. Quality function deployment (QFD) method was applied in order to understand the relationship between quality demanded and technical characteristics, leading to systematization. This method also enabled prioritization of technical characteristics, in a process named conversion. The prioritization was based on relative weight, calculated for each characteristic in conversion process. This weight represents the influence of the characteristic on production process. From the total of 114 characteristics, 64 (56%) were responsible for 80% of total relative weight calculated. It was noted that the influence of each characteristics on production process were different, although this relationship does not answer to Pareto principle. The correlation matrix, that identifies the interdependence relationship between technical characteristics, exposed the complexity and systemic nature of citriculture.

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / FundaÃÃo Cearense de Apoio ao Desenvolvimento Cientifico e TecnolÃgico / A erosÃo em parcelas irrigadas por sulcos à um fenÃmeno de especial importÃncia agrÃcola e ambiental que, tradicionalmente, no Brasil em especial, nÃo tem sido reconhecida nem tampouco estudada com detalhes. Com o objetivo de avaliar quantidade de solo erodido nesses sistemas, uma pesquisa foi conduzida em duas unidades experimentais da Fazenda Experimental Vale do Curu no municÃpio de Pentecoste (CearÃ- Brasil). As unidades experimentais foram escolhidas de acordo com texturas do solo representativas dos PerÃmetros Irrigados por superfÃcie no Nordeste brasileiro: franco arenosa e franco argilosiltosa , cada uma com 0,15 ha. Os experimentos foram conduzidos no perÃodo de julho a outubro de 2010 e de julho a outubro de 2012. Em cada unidade, nove sulcos foram analisados, correspondendo a trÃs tratamentos de vazÃo com trÃs repetiÃÃes para cada vazÃo, com um sulco-bordadura entre os sulcos-teste e dois sulcos sem irrigar delimitando os tratamentos de vazÃo. As vazÃes foram diferenciadas em funÃÃo dos diÃmetros dos sifÃes, sendo utilizados os de 1â, 1 Ââ e 2â de diÃmetro. As irrigaÃÃes foram realizadas em intervalos de no mÃximo sete dias. Durante o perÃodo do experimento foram avaliados: a concentraÃÃo de sedimentos na Ãgua de irrigaÃÃo; o efeito da perda de solo nas seguintes medidas de desempenho do sistema: eficiÃncia de aplicaÃÃo e coeficiente de uniformidade de Christiansen e os efeitos da erosÃo sobre a fertilidade do solo e a geometria da secÃÃo transversal ao fluxo de Ãgua. A pesquisa revelou que o excesso de Ãgua proveniente da irrigaÃÃo por sulcos carreou consigo altas concentraÃÃes de sedimentos, valores estes que variaram de 0,1 g L-1 a 7,0 g L-1, gerando uma perda de solo de atà 20 t ha-1ano-1. A perda de solo aumentou com a vazÃo aplicada na cabeceira do sulco segundo um modelo potencial a uma taxa crescente, entretanto, outros fatores igualmente importantes â comprimento do sulco, declividade e textura do solo â devem ser acrescidos à anÃlise, visando melhorar as prÃticas de manejo para minorar os problemas de erosÃo, otimizando assim a eficiÃncia hidrÃulica das irrigaÃÃes e a conservaÃÃo do solo. / Erosion in furrow irrigated plots is a phenomenon of agricultural and environmental importance that, particularly in Brazil, has not received the attention that deserves. Experiments were conducted in two experimental units of an experimental farm in Pentecoste (Cearà - Brazil) aiming to quantify the amount of eroded soil provoked by furrow irrigation. The experimental units were chosen to represent typical soil textures in the irrigated perimeters areas of Northeastern Brazil: sandy loam (B -T2) and silty clay loam (C - S2). Each unit had 0.15 ha. The experiments were conducted from July to October 2010 and from July to October 2012. In each unit, nine furrows were monitored, corresponding to three inflow rate treatments with three replications for each inflow rate, leaving a non-monitored furrow between the test furrows and two furrows without irrigation delimiting the flow rates treatments. Flow rates were differentiated using different diameters of siphons (1 ", 1 Â" and 2 "). Irrigations were performed at intervals of no more than seven days. During the experiments, the following variables were analyzed: sediment concentration in the runoff water, variation of the furrow cross section geometry, the relation of soil loss and system performance measures (application efficiency and ChristiansenÂs uniformity coefficient) and the effects of erosion on soil fertility. The research revealed that the excess water from the furrow irrigation carried high sediment concentrations, ranging from 0.1 g L-1 to 7.0 g L-1,resulting in a soil loss of up to 20 Mg ha-1 yr-1. Soil loss increased with the inflow rate following a potential function. Other important factors - furrow length, slope and soil texture - must be also considered to improve management that minimizes erosion and optimizes the irrigation efficiency.

Diversas atividades humanas são fortemente dependentes do clima e da sua variabilidade, especialmente aquelas relacionadas ao uso da água. A operação integrada de reservatórios com múltiplos usos requer uma série de decisões que definem quanta água deve ser alocada, ao longo do tempo para cada um dos usos, e quais os volumes dos reservatórios a serem mantidos. O conhecimento antecipado das condições climáticas resulta de vital importância para os operadores de reservatórios, pois o insumo dos reservatórios é a vazão dos rios, que por sua vez é dependente de condições atmosféricas e hidrológicas em diferentes escalas de tempo e espaço. A pesquisa trata sobre três importantes elementos de subsídio à tomada de decisão na operação de reservatórios baseada na previsão de variáveis hidrológicas: (a) as previsões de vazão de curto prazo; (b) as previsões de precipitação de longo prazo e (c) as medidas de desempenho das previsões. O reservatório de Furnas, localizado na bacia do Rio Grande, em Minas Gerais, foi selecionado como estudo de caso devido, principalmente, à disponibilidade de previsões quantitativas de chuva e pela importância desse reservatório na região analisada. A previsão de curto prazo de vazão com base na precipitação foi estimada com um modelo empírico (rede neural artificial) e a previsão de precipitação foi obtida pelo modelo regional ETA. Uma metodologia de treinamento e validação da rede neural artificial foi desenvolvida utilizando previsões perfeitas de chuva (considerando a chuva observada como previsão) e utilizando o maior número de dados disponíveis, favorecendo a representatividade dos resultados obtidos. A metodologia empírica alcançou os desempenhos obtidos com um modelo hidrológico conceitual, mostrando-se menos sensitiva aos erros na previsão quantitativa de precipitação nessa bacia. Os resultados obtidos mostraram que as previsões de vazão utilizando modelos empíricos e conceituais e incorporando previsões quantitativas de precipitação são melhores que a metodologia utilizada pelo ONS no local de estudo. A redução dos erros de previsão relativos à metodologia empregada pelo ONS foi em torno de 20% quando usadas previsões quantitativas de precipitação definidas pelo modelo regional ETA e superiores a 50% quando usadas previsões perfeitas de precipitação. Embora essas últimas previsões nunca possam ser obtidas na prática, os resultados sugerem o quanto o incremento do desempenho das previsões quantitativas de chuva melhoraria as previsões de vazão. A previsão de precipitação de longo prazo para a bacia analisada foi também estimada com um modelo empírico de redes neurais artificiais e utilizando índices climáticos como variáveis de entrada. Nesse sentido, foram estimadas previsões de precipitação acumulada no período mais chuvoso (DJF) utilizando índices climáticos associados a fenômenos climáticos, como o El Niño - Oscilação Sul e a Oscilação Decadal do Pacífico, e a modos de variabilidade climática, como a Oscilação do Atlântico Norte e o Modo Anular do Hemisfério Sul. Apesar das redes neurais artificiais terem sido aplicadas em diversos problemas relacionados a hidrometeorologia, a aplicação dessas técnicas na previsão de precipitação de longo prazo é ainda rara. Os resultados obtidos nesse trabalho mostraram que consideráveis reduções dos erros da previsão relativos ao uso apenas da média climatológica como previsão podem ser obtidos com a metodologia utilizada. Foram obtidas reduções dos erros de, no mínimo 50%, e chegando até um valor próximo a 75% nos diferentes testes efetuados no estudo de caso. Uma medida de desempenho da previsão foi desenvolvida baseada no uso de tabelas de contingência e levando em conta a utilidade da previsão. Essa medida de desempenho foi calculada com base nos resultados do uso das previsões por um modelo de operação de reservatório, e não apenas na comparação de vazões previstas e observadas. Nos testes realizados durante essa pesquisa, ficou evidente que não existe uma relação unívoca entre qualidade das previsões e utilidade das previsões. No entanto, em função de comportamentos particulares das previsões, tendências foram encontradas, como por exemplo nos modelos cuja previsão apresenta apenas defasagem. Nesses modelos, a utilidade das previsões tende a crescer na medida que a qualidade das mesmas aumenta. Por fim, uma das grandes virtudes da medida de desempenho desenvolvida nesse trabalho foi sua capacidade de distinguir o desempenho de modelos que apresentaram a mesma qualidade. / Several human activities are strongly dependent on climate and its variability, especially those related to water use. The operation of multi-purpose reservoirs systems defines how much water should be allocated and the reservoir storage volumes to be maintained, over time. Knowing in advance the weather conditions helps the decision making process, as the major inputs to reservoirs are the streamflows, which are dependent on atmospheric and hydrological conditions at different time-space scales. This research deals with three important aspects towards the decision making process of multi-purpose reservoir operation based on forecast of hydrological variables: (a) short-term streamflow forecast, (b) long-range precipitation forecast and (c) performance measures. The Furnas reservoir on the Rio Grande basin was selected as the case study, primarily because of the availability of quantitative precipitation forecasts from the Brazilian Center for Weather Prediction and Climate Studies and due to its importance in the Brazilian hydropower generation system. Short-term streamflow forecasts were estimated by an empirical model (artificial neural network – ANN) and incorporating forecast of rainfall. Quantitative precipitation forecasts (QPFs), defined by the ETA regional model, were used as inputs to the ANN models. A methodology for training and validating the ANN models was developed using perfect precipitation forecasts (i.e., using the observed precipitation as if it was a forecast) and considering the largest number of available samples, in order to increase the representativeness of the results. The empirical methodology achieved the performance obtained with a conceptual hydrological model and seemed to be less sensitive to precipitation forecast error relative to the conceptual hydrological model. Although limited to one reservoir, the results obtained show that streamflow forecasting using empirical and conceptual models and incorporating QPFs performs better than the methodology used by ONS. Reduction in the forecast errors relative to the ONS method was about 20% when using QPFs provided by ETA model, and greater than 50% when using the perfect precipitation forecast. Although the latter can never be achieved in practice, these results suggest that improving QPFs would lead to better forecasts of reservoir inflows. Long-range precipitation forecast was also estimated by an empirical model based on artificial neural networks and using climate indices as input variables. The output variable is the summer (DJF) precipitation over the Furnas watershed. It was estimated using climate indices related to climatic phenomena such as El Niño - Southern Oscillation and the Pacific Decadal Oscillation and modes of climate variability, such as the North Atlantic Oscillation and the Southern Annular Mode. Despite of ANN has been applied in several problems of hydrometeorological areas, the application of such technique for long-range precipitation forecast is still rare. The results obtained demonstrate how the methodology for seasonal precipitation forecast based on ANN can be particularly helpful, with the use of available time series of climate indices. Reductions in the forecast errors achieved by using only the climatological mean as forecast were considerable, being at least of 50% and reaching values close to 75% in several tests. A performance measure based on the use of contingency tables was developed taking into account the utility of the forecast. This performance measure was calculated based on the results of the use of the forecasts by a reservoir operation model, and not only by comparing streamflow observed and forecast. The performed tests show that there is no unequivocal relationship between quality and utility of the forecasts. However, when the forecast has a particular behavior, trends were found in the relationship between utility and quality of the forecast, such as models that generate streamflow forecast with lags in comparison to the observed values. In these models, the utility of the forecasts tends to enhance as the quality increases. Finally, the ability to distinguish the performance of forecast models having similar quality was one of the main merits of the performance measure developed in this research.

As pesquisas na área da saúde tem sido desenvolvidas com recursos governamentais obtidos junto às agências de fomento ou com recursos próprios das instituições, num esforço conjunto para a obtenção de parâmetros, em que se destacam os estudos epidemiológicos, voltados à melhoria na resolutividade dos problemas de saúde da população. A inserção da temática “Gestão em Serviços de Saúde” na área epidemiológica surge do enfoque multidisciplinar como uma forma de complementar os estudos trazendo conhecimentos oriundos da Administração para a abordagem das questões relativas à qualidade na atenção, eqüidade, identificação dos agravos de saúde, novas tecnologias e avaliação de custoefetividade das intervenções em saúde. A capacidade de auto-sustentabilidade de um laboratório de pesquisa de uma instituição pública de saúde em relação aos seus recursos financeiros e materiais e ao desenvolvimento do seu potencial humano de forma efetiva e eficaz é o foco deste trabalho que tem por objetivo desenvolver e aplicar o sistema gerencial Balanced Scorecard (BSC) no Centro de Terapia Gênica do Hospital de Clínicas de Porto Alegre. O desenvolvimento do Balanced Scorecard no Centro de Terapia Gênica foi realizado em duas etapas: a primeiro, voltada à construção do modelo conceitual através da elaboração do Mapa Estratégico e a segunda, à definição do Plano de Ação com a seleção das áreas de indicadores do laboratório. As referências usadas nessas fases foram a revisão da literatura sobre o BSC e seu desenvolvimento em outras organizações do setor público e privado, a análise exploratória dos indicadores de desempenho sugeridos pela Associação Brasileira das Instituições de Pesquisa Tecnológica (ABIPTI), o Planejamento Estratégico do Hospital de Clínicas e o levantamento dos dados do laboratório. Os resultados obtidos indicam a viabilidade da aplicação do BSC em um laboratório de pesquisa de um hospital público de ensino. Mostram, também, que a administração das atividades de pesquisa, vista sob uma perspectiva estratégica, torna a multidisciplinaridade de conhecimentos presentes em uma instituição como um fator importante para a busca das melhores práticas gerenciais que contribuam para agregar valor científico, tecnológico e econômico às atividades de pesquisa e desta forma, auxiliem a impulsionar as atividades de P&D na organização. / The researches in healthcare have been being developed with government resources or the institutions own resources, in an joint effort to obtain parameters in which the epidemiologic studies outstand , aiming at the improvement of the solution of the health problems of the population. The insertion of the issue " Administration in Healthcare Organizations " in the epidemiologic area comes from the multidisciplinary focus as a way of complementing the studies bringing knowledge originated from the Administration to the approach of issues such as the quality of the attention, access, identification of diseases, new technology and the evaluation of cost-effectiveness of the health care intervention. The capacity of self-sustainability of a research laboratory of a public institution of healthcare in relation to its financial and material resources and to the development of its human potential capital in an effective and efficient way is the focus of this work that has as objective to develop and to apply the system management called Balanced Scorecard (BSC) in the Center for Gene Therapy of the Hospital de Clinicas de Porto Alegre. The development of the Balanced Scorecard in Center for Gene Therapy was accomplished in two stages: firstly, aiming at the construction of the conceptual model through the elaboration of the Strategic Map and, secondly, to the definition of the Plan of Action with the selection of the performance measures of the laboratory. The references used in those phases were the revision of the literature on BSC and its development in other public and private organizations, the exploratory analysis of the performance measures suggested by the Brazilian Association of the Technological Research Institutions (ABIPTI), the Strategic Planning of the Hospital de Clinicas and the obtaining of the data of the laboratory. The results obtained indicate the viability of the application of BSC in a research laboratory of a public hospital. They also show that the administration of research activities, seen under a strategic perspective, turns the multidisciplinary knowledge present in an institution as an important factor for the search of the best managerial practices that contribute to join scientific, technological and economic value to the research activities and this way, help to impel the activities of P&D in the organization.

[pt] A globalização da competição tem forçado as organizações a buscar e implementar novas vertentes geradoras de vantagens competitivas. Como na atualidade a competição ocorre freqüentemente entre cadeias de suprimentos e não entre empresas isoladas, a Gestão da Cadeia de Suprimentos (Supply Chain Management - SCM) aparece como uma nova fronteira a ser explorada. Em função dessas considerações, esta dissertação tem por objetivo de aplicar o Modelo para SCM de Aragão et al. (2004) para analisar cadeias de suprimentos baseadas em dimensões-chave necessárias para uma bem sucedida SCM. As dimensões-chave consideradas neste modelo são: integração de processos de negócios, identificação dos membros-chave da cadeia, compartilhamento de informação e medidas de desempenho relacionadas à cadeia de suprimento de uma empresa fabricante de gases industriais no mercado Brasileiro. Este fabricante é considerado nesta dissertação como a empresa focal da cadeia de suprimentos. Cinco membros-chave desta cadeia são também incorporados na análise. Conclui-se que a empresa focal possui diferentes formas de gerir a sua cadeia em função de cada membro chave. / [en] The globalization of competition has forced the organizations to seek and to implement new forms to create competitive advantages. Nowadays, as competition occurs effectively between supply chains and not between isolated companies, SCM (Supply Chain Management) appears as a new frontier to be explored. Within this context, this dissertation has the objective to apply the model for SCM proposed in Aragão et al. (2004) to analyze the supply chains based on key-dimensions for a successful SCM. The key- dimensions considered in this model are: business process integrations, identification of the supply chain key-member, sharing of information and measures of performance related to the supply chain of an industrial gas manufacturer in the Brazilian market. This manufacturer is considered in this dissertation as the focal company of the supply chain. Five key-members of this chain are also included in the analysis. As a result the analyses, it is possible to conclude that the focal company develops different ways to manage its chain depending on each of its key members.

The aim of the thesis is to determine the relation between subjectively perceived and financially measured performance of a firm. The introduction of the thesis is focused on performance characteristics and measurement. Further, based on foreign and domestic researches, subjectively perceived performance is defined and comparison with objectively (financially) measured one is made. Results of the researches prove that between subjectively perceived and financially measured performance, there exists substantial to very strong statistical association. Following on an own research was carried out. As the tool for data collection serves a questionnaire sent to firms from the Czech and Slovak Republic. The data collected are compared with values of select financial measures: with ROA, ROE, ROS and Gross Sales. Said own research also confirmed a positive correlation between subjectively perceived and financially measured performance of the firm, albeit weaker than in foreign researches.

The use of data mining methods in corporate decision making has been increasing in the past decades. Its popularity can be attributed to better utilizing data mining algorithms, increased performance in computers, and results which can be measured and applied for decision making. The effective use of data mining methods to analyze various types of data has shown great advantages in various application domains. While some data sets need little preparation to be mined, whereas others, in particular high-dimensional data sets, need to be preprocessed in order to be mined due to the complexity and inefficiency in mining high dimensional data processing. Feature selection or attribute selection is one of the techniques used for dimensionality reduction. Previous research has shown that data mining results can be improved in terms of accuracy and efficacy by selecting the attributes with most significance. This study analyzes vehicle service and sales data from multiple car dealerships. The purpose of this study is to find a model that better classifies existing customers as new car buyers based on their vehicle service histories. Six different feature selection methods such as; Information Gain, Correlation Based Feature Selection, Relief-F, Wrapper, and Hybrid methods, were used to reduce the number of attributes in the data sets are compared. The data sets with the attributes selected were run through three popular classification algorithms, Decision Trees, k-Nearest Neighbor, and Support Vector Machines, and the results compared and analyzed. This study concludes with a comparative analysis of feature selection methods and their effects on different classification algorithms within the domain. As a base of comparison, the same procedures were run on a standard data set from the financial institution domain.

We consider supervised learning problems, where one is given objects with labels, and the goal is to learn a model that can make accurate predictions on new objects. These problems abound in applications, ranging from medical diagnosis to information retrieval to computer vision. Examples include binary or multiclass classi cation, where the goal is to learn a model that can classify objects into two or more categories (e.g. categorizing emails into spam or non-spam); bipartite ranking, where the goal is to learn a model that can rank relevant objects above the irrelevant ones (e.g. ranking documents by relevance to a query); class probability estimation (CPE), where the goal is to predict the probability of an object belonging to different categories (e.g. probability of an internet ad being clicked by a user). In each case, the accuracy of a model is evaluated in terms of a specified `performance measure'. While there has been much work on designing and analyzing algorithms for different supervised learning tasks, we have complete understanding only for settings where the performance measure of interest is the standard 0-1 or a loss-based classification measure. These performance measures have a simple additive structure, and can be expressed as an expectation of errors on individual examples. However, in many real-world applications, the performance measure used to evaluate a model is often more complex, and does not decompose into a sum or expectation of point-wise errors. These include the binary or multiclass G-mean used in class-imbalanced classification problems; the F1-measure and its multiclass variants popular in text retrieval; and the (partial) area under the ROC curve (AUC) and precision@ employed in ranking applications. How does one design efficient learning algorithms for such complex performance measures, and can these algorithms be shown to be statistically consistent, i.e. shown to converge in the limit of infinite data to the optimal model for the given measure? How does one develop efficient learning algorithms for complex measures in online/streaming settings where the training examples need to be processed one at a time? These are questions that we seek to address in this thesis. Firstly, we consider the bipartite ranking problem with the AUC and partial AUC performance measures. We start by understanding how bipartite ranking with AUC is related to the standard 0-1 binary classification and CPE tasks. It is known that a good binary CPE model can be used to obtain both a good binary classification model and a good bipartite ranking model (formally, in terms of regret transfer bounds), and that a binary classification model does not necessarily yield a CPE model. However, not much is known about other directions. We show that in a weaker sense (where the mapping needed to transform a model from one problem to another depends on the underlying probability distribution), a good bipartite ranking model for AUC can indeed be used to construct a good binary classification model, and also a good binary CPE model. Next, motivated by the increasing number of applications (e.g. biometrics, medical diagnosis, etc.), where performance is measured, not in terms of the full AUC, but in terms of the partial AUC between two false positive rates (FPRs), we design batch algorithms for optimizing partial AUC in any given FPR range. Our algorithms optimize structural support vector machine based surrogates, which unlike for the full AUC; do not admit a straightforward decomposition into simpler terms. We develop polynomial time cutting plane solvers for solving the optimization, and provide experiments to demonstrate the efficacy of our methods. We also present an application of our approach to predicting chemotherapy outcomes for cancer patients, with the aim of improving treatment decisions. Secondly, we develop algorithms for optimizing (surrogates for) complex performance mea-sures in the presence of streaming data. A well-known method for solving this problem for standard point-wise surrogates such as the hinge surrogate, is the stochastic gradient descent (SGD) method, which performs point-wise updates using unbiased gradient estimates. How-ever, this method cannot be applied to complex objectives, as here one can no longer obtain unbiased gradient estimates from a single point. We develop a general stochastic method for optimizing complex measures that avoids point-wise updates, and instead performs gradient updates on mini-batches of incoming points. The method is shown to provably converge for any performance measure that satis es a uniform convergence requirement, such as the partial AUC, precision@ and F1-measure, and in experiments, is often several orders of magnitude faster than the state-of-the-art batch methods, while achieving similar or better accuracies. Moreover, for specific complex binary classification measures, which are concave functions of the true positive rate (TPR) and true negative rate (TNR), we are able to develop stochastic (primal-dual) methods that can indeed be implemented with point-wise updates, by using an adaptive linearization scheme. These methods admit convergence rates that match the rate of the SGD method, and are again several times faster than the state-of-the-art methods. Finally, we look at the design of consistent algorithms for complex binary and multiclass measures. For binary measures, we consider the practically popular plug-in algorithm that constructs a classifier by applying an empirical threshold to a suitable class probability estimate, and provide a general methodology for proving consistency of these methods. We apply this technique to show consistency for the F1-measure, and under a continuity assumption on the distribution, for any performance measure that is monotonic in the TPR and TNR. For the case of multiclass measures, a simple plug-in method is no longer tractable, as in the place of a single threshold parameter, one needs to tune at least as many parameters as the number of classes. Using an optimization viewpoint, we provide a framework for designing learning algorithms for multiclass measures that are general functions of the confusion matrix, and as an instantiation, provide an e cient and provably consistent algorithm based on the bisection method for multiclass measures that are ratio-of-linear functions of the confusion matrix (e.g. micro F1). The algorithm outperforms the state-of-the-art SVMPerf method in terms of both accuracy and running time. Overall, in this thesis, we have looked at various aspects of complex performance measures used in supervised learning problems, leading to several new algorithms that are often significantly better than the state-of-the-art, to improved theoretical understanding of the performance measures studied, and to novel real-life applications of the algorithms developed.

Effective maintenance planning is essential and important in any organisation that is responsible for procuring and managing complex assets. In the marine shipping industry maintenance planning is very significant due to its complexity and the obligations on shipping organisations to comply with certain regulations and requirements. Moreover, improper planning can reduce the ship's availability, which may in turn, be reflected in the revenue of the company. Another issue that requires attention in this field is the cost of maintenance, since improper or inadequate planning could result in breakdowns that could increase the cost of maintenance.This research aims to identify the key factors that affect ship maintenance planning and to provide a framework that can help the decision maker to identify and choose optimum decisions regarding ship maintenance. The research is divided into four stages in order to achieve its objectives and to address the research problem.The first stage is the review of the literature to identify the need for maintenance and to select the key factors that affect maintenance planning. The findings indicate that: maintenance scheduling, selection of maintenance strategy, ship construction, crew compensation, and shipyard selection are the most important factors.The second stage is to evaluate maintenance performance measurements for the marine shipping industry by conducting case study and interviews with professionals involved in the mercantile industry. Semi-structured interviews were conducted with six senior staff experts from three different organisations. The results show that: dry docking scheduling, maintenance costs and budgets, customer satisfaction, employees' satisfaction, classification requirements, and the ship's maintenance requirements are the main factors that have great influence on maintenance planning.The third stage is to develop new methodology to measure the maintenance performance in the marine shipping organisation which is the ship maintenance performance measurement (SMPM) framework. The developed method was validated to assist managers in making the right decisions in ship maintenance planning. The framework was developed based on ten thematic criteria that can be used as indicators for potential organisation growth, i.e., maintenance strategy; dry docking scheduling; budget and costs; the ship's equipment; customer satisfaction; employees; health, safety and environment; learning and growth; classification requirements; and the ship's operation and demands requirements. Interviews were conducted with key personnel from the Kuwait Oil Tanker Company (KOTC) to validate the framework.The fourth stage demonstrates that an optimised schedule for the dry docking of ships for routine maintenance has been constructed. This is accomplished on the basis of one measured criterion, dry docking scheduling, by using an integer programming model to maximise the ship's availability within the company fleet. The model is defined by three constraints: the maintenance window, maintenance completion, and the ship's limit. The model was validated using data from KOTC, and the results depict an optimum solution for maintenance scheduling, maximising the ship's availability to 100% and not less than 92%.