<p>Visualization of large scale time-varying volumetric datasets is an active topic of research. Technical limitations in terms of bandwidth and memory usage become a problem when visualizing these datasets on commodity computers at interactive frame rates. The overall objective is to overcome these limitations by adapting the methods of an existing Direct Volume Rendering pipeline. The objective is considered to be a proof of concept to assess the feasibility of visualizing large scale time-varying datasets using this pipeline. The pipeline consists of components from previous research, which make extensive use of graphics hardware to visualize large scale static data on commodity computers.</p><p>This report presents a diploma work, which adapts the pipeline to visualize flow features concealed inside the large scale Computational Fluid Dynamics dataset. The work provides a foundation to address the technical limitations of the commodity computer to visualize time-varying datasets. The report describes the components making up the Direct Volume Rendering pipeline together with the adaptations. It also briefly describes the Computational Fluid Dynamics simulation, the flow features and an earlier visualization approach to show the system’s limitations when exploring the dataset.</p>

Visualization is the process of making something perceptible to the mind or imagination. The techniques for producing visual imagery of volumetric data have advanced immensely during the last decades to a point where each produced image can include an overwhelming amount of information. An increasingly viable solution to the limitations of the human sense of visual perception is to make use of not only vision, but also additional senses. This thesis presents recent work on the development of principles and algorithms for generating representations of volumetric data through the sense of touch for the purpose of visualization. The primary idea introduced in this work is the concept of yielding constraints, that can be used to provide a continuous set of shapes as a representation of features of interest in various types of volumetric data. Some of the earlier identified standard human exploratory procedures can then be used which enables natural, intuitive and effective interaction with the data. The yielding constraints concept is introduced, and an algorithm based on haptic primitives is described, which forms a powerful yet versatile implementation of the yielding constraints. These methods are also extended to handle time-varying, moving and low quality data. A framework for multimodal visualization has been built on the presented methods, and this is used to demonstrate the applicability and versatility of the work through several example applications taken from different areas.

Visualization of large scale time-varying volumetric datasets is an active topic of research. Technical limitations in terms of bandwidth and memory usage become a problem when visualizing these datasets on commodity computers at interactive frame rates. The overall objective is to overcome these limitations by adapting the methods of an existing Direct Volume Rendering pipeline. The objective is considered to be a proof of concept to assess the feasibility of visualizing large scale time-varying datasets using this pipeline. The pipeline consists of components from previous research, which make extensive use of graphics hardware to visualize large scale static data on commodity computers. This report presents a diploma work, which adapts the pipeline to visualize flow features concealed inside the large scale Computational Fluid Dynamics dataset. The work provides a foundation to address the technical limitations of the commodity computer to visualize time-varying datasets. The report describes the components making up the Direct Volume Rendering pipeline together with the adaptations. It also briefly describes the Computational Fluid Dynamics simulation, the flow features and an earlier visualization approach to show the system’s limitations when exploring the dataset.

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Data can be found everywhere, for example in the form of price, size, weight and colour of all products sold by a company, or as time series of daily observations of temperature, precipitation, wind and visibility from thousands of stations. Due to their size and complexity it is intrinsically hard to form a global overview and understanding of them. Information visualization aims at overcoming these difficulties by transforming data into representations that can be more easily interpreted. This thesis presents work on the development of methods to enable efficient information visualization of multivariate and time-varying data sets by conveying information in a clear and interpretable way, and in a reasonable time. The work presented is primarily based on a popular multivariate visualization technique called parallel coordinates but many of the methods can be generalized to apply to other information visualization techniques. A three-dimensional, multi-relational version of parallel coordinates is presented that enables a simultaneous analysis of all pairwise relationships between a single focus variable and all other variables included in the display. This approach permits a more rapid analysis of highly multivariate data sets. Through a number of user studies the multi-relational parallel coordinates technique has been evaluated against standard, two-dimensional parallel coordinates and been found to better support a number of different types of task. High precision density maps and transfer functions are presented as a means to reveal structure in large data displayed in parallel coordinates. These two approaches make it possible to interactively analyse arbitrary regions in a parallel coordinates display without risking the loss of significant structure. Another focus of this thesis relates to the visualization of time-varying, multivariate data. This has been studied both in the specific application area of system identification using volumetric representations, as well as in the general case by the introduction of temporal parallel coordinates. The methods described in this thesis have all been implemented using modern computer graphics hardware which enables the display and manipulation of very large data sets in real time. A wide range of data sets, both synthetically generated and taken from real applications, have been used to test these methods. It is expected that, as long as the data have multivariate properties, they could be employed efficiently.

Ferramentas e técnicas de visualização promovem uma análise de dados mais efetiva pelo fato de explorar a capacidade humana na percepção de padrões, principalmente em representações gráficas. Muitos fenômenos são associados a algum tipo de referência, temporal ou geográfica, que pode oferecer informação importante quando são submetidos a processos de análise. Este trabalho aborda representações visuais de dados geradas por técnicas de projeção multidimensional, e propõe uma estratégia para o tratamento diferenciado das referências temporais ou geográficas presentes em conjuntos de dados, no processo de gerar uma projeção multidimensional. Foi proposta e implementada uma variação da técnica Least Square Projection (LSP) que evidencia a informação das referências e permite ao usuário interagir com os mapas visuais gerados, bem como diversas funcionalidades que auxiliam no processo de análise exploratória. A nova abordagem é ilustrada por meio de estudos de caso envolvendo bases de dados temporais e com referências geográficas, em que foi possível observar o comportamento global dos elementos, bem como comportamentos de elementos ou grupos de elementos de interesse. Limitações da estratégia proposta também são discutidas / Visualization tools and techniques promote more effective data analysis by exploiting the human visual perception capabilities in detecting patterns in graphical representations. Many phenomena generate data that include temporal or geographical references, which are likely to provide important information in data analysis procedures. This work addresses data visualizations generated with multidimensional projections, proposing a strategy to handle temporal and geographical references present in multidimensional data sets, when generating multidimensional projections. The Least Squares Projection (LSP) technique was extended to explicitly handle the reference information and represent it in the visual maps, and a set of supporting analysis functions have been implemented. The proposed approach is illustrated through case studies on multidimensional data sets, in which it was possible to observe the global behavior of the elements, as well as individual behavior of elements or groups of elements of interest

Ferramentas e técnicas de visualização promovem uma análise de dados mais efetiva pelo fato de explorar a capacidade humana na percepção de padrões, principalmente em representações gráficas. Muitos fenômenos são associados a algum tipo de referência, temporal ou geográfica, que pode oferecer informação importante quando são submetidos a processos de análise. Este trabalho aborda representações visuais de dados geradas por técnicas de projeção multidimensional, e propõe uma estratégia para o tratamento diferenciado das referências temporais ou geográficas presentes em conjuntos de dados, no processo de gerar uma projeção multidimensional. Foi proposta e implementada uma variação da técnica Least Square Projection (LSP) que evidencia a informação das referências e permite ao usuário interagir com os mapas visuais gerados, bem como diversas funcionalidades que auxiliam no processo de análise exploratória. A nova abordagem é ilustrada por meio de estudos de caso envolvendo bases de dados temporais e com referências geográficas, em que foi possível observar o comportamento global dos elementos, bem como comportamentos de elementos ou grupos de elementos de interesse. Limitações da estratégia proposta também são discutidas / Visualization tools and techniques promote more effective data analysis by exploiting the human visual perception capabilities in detecting patterns in graphical representations. Many phenomena generate data that include temporal or geographical references, which are likely to provide important information in data analysis procedures. This work addresses data visualizations generated with multidimensional projections, proposing a strategy to handle temporal and geographical references present in multidimensional data sets, when generating multidimensional projections. The Least Squares Projection (LSP) technique was extended to explicitly handle the reference information and represent it in the visual maps, and a set of supporting analysis functions have been implemented. The proposed approach is illustrated through case studies on multidimensional data sets, in which it was possible to observe the global behavior of the elements, as well as individual behavior of elements or groups of elements of interest

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Made available in DSpace on 2016-03-15T19:37:44Z (GMT). No. of bitstreams: 1 Diego Gadens dos Santos.pdf: 2663525 bytes, checksum: 46be86494cd52896593a08e979b2a0ce (MD5) Previous issue date: 2012-09-14 / Fundo Mackenzie de Pesquisa / Current technologies have made it possible to generate and store data in high volumes. To process and collect information in large databases is not always as easy as creating them. Therefore, this gap has stimulated the search for efficient techniques capable of extracting useful and non-trivial knowledge, which are intrinsic to these large data sets. The goal of this work is to propose a bioinspired algorithm, based on the Boids artificial life model, which will be used to group data in dynamic environments, i.e. in databases updated over time. The Bo-ids algorithm was originally created to illustrate the simulation of the coordinated movement observed in a flock of birds and other animals. Thus, to use this algorithm for data clustering, some modifications must be applied. These changes will be implemented in the classical rules of cohesion, separation and alignment of the Boids model in order to consider the distance (similarity/dissimilarity) among objects. Thus, it creates objects that stand and move around the space, representing the natural groups within the data, and it is expected that similar ob-jects tend to form dynamic clusters (groups) of Boids in the environment, while dissimilar objects tend to keep a larger distance between them. The results presented attest the robust-ness of the algorithm for clustering time-varying data under the light of different evaluation measures and in various databases from the literature. / A capacidade de geração e armazenamento de dados proporcionada pelas tecnologias atuais levou ao surgimento de bases de dados com uma grande variedade de tipos e tamanhos. Extra-ir conhecimentos não triviais e úteis a partir de grandes bases de dados, entretanto, é uma tare-fa muito mais difícil do que a criação das mesmas. Esta lacuna tem estimulado a busca por técnicas eficientes de extração de conhecimentos intrínsecos a estes grandes conjuntos de da-dos, capazes de permitir tomadas estratégicas de decisão. Dentre as muitas tarefas da extração de conhecimentos a partir de dados, tem-se o agrupamento, que consiste na segmentação da base em grupos cujos objetos são mais parecidos entre si do que a objetos pertencentes a ou-tros grupos. Apesar de a área ser bastante ativa, pouco tem sido feito no sentido de desenvol-ver e investigar algoritmos de agrupamento para dados variantes no tempo, por exemplo, tran-sações financeiras, dados climáticos, informações e mensagens postadas em redes sociais e muitos outros. Tendo em vista a relevância prática desse tipo de análise e o crescente interesse pelos algoritmos inspirados na biologia, este trabalho tem como objetivo propor um algoritmo bioinspirado, baseado no modelo de vida artificial de Boids, para realizar o agrupamento de dados variantes no tempo. O algoritmo de Boids foi inicialmente criado para demonstrar ape-nas a simulação da movimentação coordenada observada em uma revoada de pássaros. A fim de utilizar este algoritmo para a tarefa de agrupamento de dados, algumas modificações tive-ram de ser propostas nas regras clássicas de coesão, separação e alinhamento dos Boids. Desta forma, foram criados objetos que se posicionam e se movimentam no espaço, de maneira a representar os grupos naturais existentes nos dados. A característica dinâmica intrínseca dos Boids tornou o algoritmo proposto, denominado dcBoids (dynamic clustering Boids), um can-didato natural para a resolução de problemas de agrupamento de dados variantes no tempo. Os resultados obtidos atestaram a robustez do método em seu contexto de aplicação, sob a pers-pectiva de diferentes medidas de avaliação de desempenho e quando aplicado a várias bases de dados da literatura com dinâmicas inseridas artificialmente.

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