Information uncertainty is inherent in many real-world problems and adds a layer of complexity to modeling and visualization tasks. This often causes users to ignore uncertainty, especially when it comes to visualization, thereby discarding valuable knowledge. A coherent framework for the modeling and visualization of information uncertainty is needed to address this issue In this work, we have identified four major barriers to the uptake of uncertainty modeling and visualization. Firstly, there are numerous uncertainty modeling tech- niques and users are required to anticipate their uncertainty needs before building their data model. Secondly, parameters of uncertainty tend to be treated at the same level as variables making it easy to introduce avoidable errors. This causes the uncertainty technique to dictate the structure of the data model. Thirdly, propagation of uncertainty information must be manually managed. This requires user expertise, is error prone, and can be tedious. Finally, uncertainty visualization techniques tend to be developed for particular uncertainty types, making them largely incompatible with other forms of uncertainty information. This narrows the choice of visualization techniques and results in a tendency for ad hoc uncertainty visualization. The aim of this thesis is to present an integrated information uncertainty modeling and visualization environment that has the following main features: information and its uncertainty are encapsulated into atomic variables, the propagation of uncertainty is automated, and visual mappings are abstracted from the uncertainty information data type. Spreadsheets have previously been shown to be well suited as an approach to visu- alization. In this thesis, we devise a new paradigm extending the traditional spreadsheet to intrinsically support information uncertainty.Our approach is to design a framework that integrates uncertainty modeling tech- niques into a hierarchical order based on levels of detail. The uncertainty information is encapsulated and treated as a unit allowing users to think of their data model in terms of the variables instead of the uncertainty details. The system is intrinsically aware of the encapsulated uncertainty and is therefore able to automatically select appropriate uncertainty propagation methods. A user-objectives based approach to uncertainty visualization is developed to guide the visual mapping of abstracted uncertainty information. Two main abstractions of uncertainty information are explored for the purpose of visual mapping: the Unified Uncertainty Model and the Dual Uncertainty Model. The Unified Uncertainty Model provides a single view of uncertainty for visual mapping, whereas the Dual Uncertainty Model distinguishes between possibilistic and probabilistic views. Such abstractions provide a buffer between the visual mappings and the uncertainty type of the underly- ing data, enabling the user to change the uncertainty detail without causing the visual- ization to fail. Two main case studies are presented. The first case study covers exploratory and forecasting tasks in a business planning context. The second case study inves- tigates sensitivity analysis for financial decision support. Two minor case studies are also included: one to investigate the relevancy visualization objective applied to busi- ness process specifications, and the second to explore the extensibility of the system through General Purpose Graphics Processor Unit (GPGPU) use. A quantitative anal- ysis compares our approach to traditional analytical and numerical spreadsheet-based approaches. Two surveys were conducted to gain feedback on the from potential users. The significance of this work is that we reduce barriers to uncertainty modeling and visualization in three ways. Users do not need a mathematical understanding of the uncertainty modeling technique to use it; uncertainty information is easily added, changed, or removed at any stage of the process; and uncertainty visualizations can be built independently of the uncertainty modeling technique.

As the trend of successful network attacks continue to rise, better forms of intrusion, detection and prevention are needed. This thesis addresses network traffic visualization techniques that aid administrators in recognizing attacks. A view of port statistics and Intrusion Detection System (IDS) alerts has been developed. Each help to address issues with analyzing large datasets involving networks. Due to the amount of traffic as well as the range of possible port numbers and IP addresses, scaling techniques are necessary. A port-based overview of network activity produces an improved representation for detecting and responding to malicious activity. We have found that presenting an overview using stacked histograms of aggregate port activity, combined with the ability to drill-down for finer details allows small, yet important details to be noticed and investigated without being obscured by large, usual traffic. Another problem administrators face is the cumbersome amount of alarm data generated from IDS sensors. As a result, important details are often overlooked, and it is difficult to get an overall picture of what is occurring in the network by manually traversing textual alarm logs. We have designed a novel visualization to address this problem by showing alarm activity within a network. Alarm data is presented in an overview from which system administrators can get a general sense of network activity and easily detect anomalies. They additionally have the option of then zooming and drilling down for details. Based on our system administrator requirements study, this graphical layout addresses what system administrators need to see, is faster and easier than analyzing text logs, and uses visualization techniques to effectively scale and display the data. With this design, we have built a tool that effectively uses operational alarm log data generated on the Georgia Tech campus network. For both of these systems, we describe the input data, the system design, and examples. Finally, we summarize potential future work.

Data abstraction techniques are widely used in multiresolution visualization systems to reduce visual clutter and facilitate analysis from overview to detail. However, analysts are usually unaware of how well the abstracted data represent the original dataset, which can impact the reliability of results gleaned from the abstractions. In this thesis, we define three types of data abstraction quality measures for computing the degree to which the abstraction conveys the original dataset: the Histogram Difference Measure, the Nearest Neighbor Measure and Statistical Measure. They have been integrated within XmdvTool, a public-domain multiresolution visualization system for multivariate data analysis that supports sampling as well as clustering to simplify data. Several interactive operations are provided, including adjusting the data abstraction level, changing selected regions, and setting the acceptable data abstraction quality level. Conducting these operations, analysts can select an optimal data abstraction level. We did an evaluation to check how well the data abstraction measures conform to the data abstraction quality perceived by users. We adjusted the data abstraction measures based on the results of the evaluation. We also experimented on the measures with different distance methods and different computing mechanisms, in order to find the optimal variation from many variations of each type of measure. Finally, we developed two case studies to demonstrate how analysts can compare different abstraction methods using the measures to see how well relative data density and outliers are maintained, and then select an abstraction method that meets the requirement of their analytic tasks.

A área de visualização volumétrica engloba um conjunto de técnicas utilizadas na representação, manipulação e exibição de dados associados à região de um volume, possibilitando, assim, a exploração e melhor compreensão do interior de objetos de natureza tridimensional. Contudo, algumas limitações ainda são encontradas nessa área, como, por exemplo, a exploração de mais de um valor simultaneamente em conjuntos de dados volumétricos multivariados. Além desse desafio, outro objeto de grande interesse da comunidade científica é a exploração de volumes variantes no tempo. A complexidade nesse caso está em tratar ou processar uma quantidade muito grande de dados buscando descobrir propriedades, estruturas ou características que variam com o tempo. O presente trabalho propõe técnicas e abordagens, baseadas no conceito de projeções multidimensionais, visando dar apoio à análise de conjuntos volumétricos multivariados que variam no tempo. A primeira técnica proposta, denominada Fastmap*, possibilitou a projeção de espaços de alta dimensionalidade em fluxo contínuo. A segunda técnica apresentada, denominada RLNP, permitiu a projeção de dados por vizinhança mantendo a coerência temporal nos dados projetados, além de possuir a capacidade de projetar espaços de alta dimensão com um nível de stressbaixo. Também, propomos uma abordagem para a análise baseada em atributos, denominada Scatter Projection, que facilita a exploração focada em um atributo específico junto com a similaridade dos dados entre eles. Finalmente, propõe-se uma abordagem baseada na reprojeção de agrupamentos usando técnicas de seleção de atributos para tentar identificar melhor as estruturas internas do volume. Assim, o presente trabalho contribui no sentido de levantar e discutir limitações das técnicas disponíveis, e em seguida, buscar possibilidades de solução para tais questões, propondo técnicas e abordagens que possibilitam a exploração de grandes conjuntos de dados volumétricos multivariados, mantendo a coerência temporal / The area of volume visualization encompasses a set of techniques used for representation, manipulation and display of data associated with a region of a volume, thus enabling the exploration and understanding of the interior of three-dimensional objects. However, some limitations are still encountered in this area. For example, the simultaneous exploration of more than one value in multivariate volumetric datasets. Beyond this challenge, another issue of great interest to the scientific community is the exploration of time-varying volumes. The complexity of this case lies in treatment or processing of a very large amount of data, seeking to discover properties, structures, or characteristics that may vary in time. This work proposes techniques and approaches, based on the concept of multidimensional projections, in order to support multivariate volumetric analysis of time varying data sets. The first technique proposed, called Fastmap*, enables the projection of high dimensional streaming data. The second technique presented, called Recursive Laplacian-based Neiboorhood Projection, allows the projection of data sets based on neighborhoods, maintaining the temporal coherence in the projected data, besides having the ability to project highdimensional spaces with a low level of stress. Also, we propose an approach for the analysis of specific attributes, referred to as Scatter Projection, which facilitates the exploration focused on a specific attribute and on the similarity between them. Finally, we propose an approach based on reprojection of groups using feature selection techniques for better identification of internal structures of the volume. Thus, this study contributes towards surveying and discussing limitations of the area, and then seeks ways of solving these issues, proposing techniques and approaches that enable the exploration of multidimensional volumetric time varying data sets, maintaining the temporal coherence

<p>Brain connectome network visualization could help the neurologists inspect the brain structure easily and quickly. In the thesis, the model of the brain connectome network is visualized in both three dimensions (3D) environment and two dimensions (2D) environment. One is named “Brain Explorer for Connectomic Analysis” (BECA) developed by the previous research already. It could present the 3D model of brain structure with region of interests (ROIs) in different colors [5]. The other is mainly for the information visualization of brain connectome in 2D. It adopts the force-directed layout to visualize the network. However, the brain network visualization could not bring the user intuitively ideas about brain structure. Sometimes, with the increasing scales of ROIs (nodes), the visualization would bring more visual clutter for readers [3]. So, brain connectome network properties visualization becomes a useful complement to brain network visualization. For a better understanding of the effect of Alzheimer’s disease on the brain nerves, the thesis introduces several methods about the brain graph properties visualization. There are the five selected graph properties discussed in the thesis. The degree and closeness are node properties. The shortest path, maximum flow, and clique are edge properties. Except for clique, the other properties are visualized in both 3D and 2D. The clique is visualized only in 2D. For the clique, a new hypergraph visualization method is proposed with three different algorithms. Instead of using an extra node to present a clique, the thesis uses a “belt” to connect all nodes within the same clique. The methods of node connections are based on the traveling salesman problem (TSP) and Law of cosines. In addition, the thesis also applies the result of the clique to adjust the force-directed layout of brain graph in 2D to dramatically eliminate the visual clutter. Therefore, with the support of the graph properties visualization, the brain connectome network visualization tools become more flexible.</p>

Graph visualization plays an increasingly important role in software engineering and information systems. Examples include UML, E-R diagrams, database structures, visual programming, web visualization, network protocols, molecular structures, genome diagrams, and social structures. Many classical algorithms for graph visualization have already been developed over the past decades. However, these algorithms face difficulties in practice, such as the overlapping nodes, large graph layout, and dynamic graph layout. In order to solve these problems, this research aims to systematically address both algorithmic and approach issues related to a novel framework that describes the process of graph visualization applications. At the same time, all the proposed algorithms and approaches can be applied to other situations as well. First of all, a framework for graph visualization is described, along with a generic approach to the graphical representation of a relational information source. As the important parts of this framework, two main approaches, Filtering and Clustering, are then particularly investigated to deal with large graph layouts effectively. In order to filter 'noise' or less important nodes in a given graph, two new methods are proposed to compute importance scores of nodes called NodeRank, and then to control the appearances of nodes in a layout by ranking them. Two novel algorithms for clustering graphs, KNN and SKM, are developed to reduce visual complexity. Identifying seed nodes as initial members of clusters, both algorithms make use of either the k-nearest neighbour search or a novel node similarity matrix to seek groups of nodes with most affinities or similarities among them. Such groups of relatively highly connected nodes are then replaced with abstract nodes to form a coarse graph with reduced dimensions. An approach called MMD to the layout of clustered graphs is provided using a multiple-window�multiple-level display. As for the dynamic graph layout, a new approach to removing overlapping nodes called Force-Transfer algorithm is developed to greatly improve the classical Force- Scan algorithm. Demonstrating the performance of the proposed algorithms and approaches, the framework has been implemented in a prototype called PGD. A number of experiments as well as a case study have been carried out.

<p><p>This paper focus on the development of a tool for Ship and Weather Information Monitoring (SWIM) visualizing weather data combined with data from ship voyages. The project was done in close collaboration with the Swedish Meteorological and Hydrological Institute (SMHI) who also evaluated the result. The goal was to implement a tool which will help shipping companies to monitor their feet and the weather development along planned routes and provide support for decisions regarding route choice and to evade hazard. A qualitative usability study was performed to gather insight about usability issues and to aid future development. Overall the result of the study was positive and the users felt that the tool would aid them in the daily work.</p></p>

<p>It is important for the military to know as much as possible about how easily detected their vehicles are. One way among many used to detect vehicles is the use of radar sensors. The radar reflecting characteristics of military vehicles are therefor often rigorously tested. With measurements and simulations it is possible to calculate likely detection distances to a vehicle from different angles. This process often produces very large data sets that are hard to analyze.</p><p>This thesis discusses and implements a method for visualizing the detection distance data set and also discusses a lot of related issues with a focus on computer graphics.</p><p>The main concept is called spherical displacement and the idea is to visualize the detection distances as a surface with the imagined vehicle in the center point. Detection is likely inside the surface but not on the outside. This concept is the next step from the colored sphere where the colors represent the detection distance which was previously used.</p><p>The thesis project resulted in a visualization tool that uses the new concept and can handle large data sets. The spherical displacement concept is more intuitive and shows detail better than the colored sphere visualization.</p>

Statistical flow data such as commuting, migration, trade and money flows has gained manyinterests from policy makers, city planners, researchers and ordinary citizens as well. Therehave appeared numerous statistical data visualisations; however, there is a shortage of applicationsfor visualising flow data. Moreover, among these rare applications, some are standaloneand only for expert usages, some do not support interactive functionalities, and somecan only provide an overview of data. Therefore, in this thesis, I develop a web-enabled,highly interactive and analysis support statistical flow data visualisation application that addressesall those challenges.My application is implemented based on GAV Flash, a powerful interactive visualisationcomponent framework, thus it is inherently web-enabled with basic interactive features. Theapplication uses visual analytics approach that combines both data analysis and interactivevisualisation to solve cluttering issue, the problem of overlapping flows on the display. A varietyof analysis means are provided to analyse flow data efficiently including analysing bothflow directions simultaneously, visualising time-series flow data, finding most attracting regionsand figuring out the reason behind derived patterns. The application also supportssharing knowledge between colleagues by providing story-telling mechanism which allowsusers to create and share their findings as a visualisation story. Last but not least, the applicationenables users to embed the visualisation based on the story into an ordinary web-pageso that public stand a golden chance to derive an insight into officially statistical flow data.

We present a novel approach to text visualization called descriptive non-photorealistic rendering which exploits the inherent spatial and abstract dimensions in text documents to integrate 3D non-photorealistic rendering with information visualization. The visualization encodes text data onto 3D models, emphasizing the relative signi ficance of words in the text and the physical, real-world relationships between those words. Analytic exploration is supported through a collection of interactive widgets and direct multitouch interaction with the 3D models. We applied our method to analyze a collection of vehicle complaint reports from National Highway Traffic Safety Administration (NHTSA), and through a qualitative evaluation study, we demonstrate how our system can support tasks such as comparing the reliability of di fferent makes and models, finding interesting facts, and revealing possible causal relations between car parts. / UOIT