abstract: The proper quantification and visualization of uncertainty requires a high level of domain knowledge. Despite this, few studies have collected and compared the roles, experiences and opinions of scientists in different types of uncertainty analysis. I address this gap by conducting two types of studies: 1) a domain characterization study with general questions for experts from various fields based on a recent literature review in ensemble analysis and visualization, and; 2) a long-term interview with domain experts focusing on specific problems and challenges in uncertainty analysis. From the domain characterization, I identified the most common metrics applied for uncertainty quantification and discussed the current visualization applications of these methods. Based on the interviews with domain experts, I characterized the background and intents of the experts when performing uncertainty analysis. This enables me to characterize domain needs that are currently underrepresented or unsupported in the literature. Finally, I developed a new framework for visualizing uncertainty in climate ensembles. / Dissertation/Thesis / Masters Thesis Computer Science 2016

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.

The increasing number of web services published over the Web highlights the need for an effective method for users to find appropriate web services. Existing web service discovery methods do not effectively aid a user in finding suitable web services. The current methods provide textual lists of web services that the user is required to explore and manually evaluate. Thus, these methods lead to time-consuming and ineffective web service discovery. The aim of this research was to investigate using information visualization (IV) techniques to effectively support web service discovery. The node-and-link network IV technique was selected as the most appropriate IV technique to visualize web service collections. A prototype, called SerViz, was developed as a tool for interactive visualization of web service collections incorporating the node-and-link IV technique and an alphabetical list-based technique. SerViz used the Programmable Web web service collection as the sample web service collection. A usability evaluation was conducted to compare these techniques. Ninety percent of participants preferred the network IV technique for visualizing web service collections. The network IV technique was also faster for browsing. Several usability problems were identified with the network IV technique. This motivated a need for implementing an alternative IV technique in SerViz. The node-and-link tree IV technique was selected as it was more structured than the network IV technique. A usability evaluation was conducted to compare the network and tree IV techniques. Participants slightly preferred the tree IV technique as the technique to visualize web service collections. The tree IV technique was faster for browsing the web service collection while the network IV technique was faster for searching and filtering. This research has determined that IV techniques can be used to effectively support web service discovery. Future work will involve using IV techniques to support collaborative web service discovery. Keywords: Web Service Discovery, Information Visualization, Web Service Collections, Information Visualization Techniques.

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

Analysis and visualization of large data sets is time consuming and sometimes can be a very difficult process, especially for 3D data sets. Therefore, data processing and visualization techniques have often been used in the case of different massive data analysis for efficiency and accuracy purposes. This thesis presents a multi-scale data sketching solution, specifically for large 3D scientific data with a goal to support collaborative data management, analysis and visualization. The idea is to allow users to quickly identify interesting regions and observe significant patterns without directly accessing the raw data, since most of the information in raw form is not useful. This solution will provide a fast way to allow the users to choose the regions they are interested and save time. By preprocessing the data, our solution can sketch out the general regions of the 3D data, and users can decide whether they are interested in going further to analyze the current data. The key issue is to find efficient and accurate algorithms to detect boundaries or regions information for large 3D scientific data. Specific techniques and performance analysis are also discussed.

Indiana University-Purdue University Indianapolis (IUPUI) / 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.

While uncertainty in scientific data attracts an increasing research interest in the visualization community, two critical issues remain insufficiently studied: (1) visualizing the impact of the uncertainty of a data set on its features and (2) interactively exploring 3D or large 2D data sets with uncertainties. In this study, a suite of feature-based techniques is developed to address these issues. First, a framework of feature-level uncertainty visualization is presented to study the uncertainty of the features in scalar and vector data. The uncertainty in the number and locations of features such as sinks or sources of vector fields are referred to as feature-level uncertainty while the uncertainty in the numerical values of the data is referred to as data-level uncertainty. The features of different ensemble members are indentified and correlated. The feature-level uncertainties are expressed as the transitions between corresponding features through new elliptical glyphs. Second, an interactive visualization tool for exploring scalar data with data-level and two types of feature-level uncertainties — contour-level and topology-level uncertainties — is developed. To avoid visual cluttering and occlusion, the uncertainty information is attached to a contour tree instead of being integrated with the visualization of the data. An efficient contour tree-based interface is designed to reduce users’ workload in viewing and analyzing complicated data with uncertainties and to facilitate a quick and accurate selection of prominent contours. This thesis advances the current uncertainty studies with an in-depth investigation of the feature-level uncertainties and an exploration of topology tools for effective and interactive uncertainty visualizations. With quantified representation and interactive capability, feature-based visualization helps people gain new insights into the uncertainties of their data, especially the uncertainties of extracted features which otherwise would remain unknown with the visualization of only data-level uncertainties.

Visualization and exploration of nematic liquid crystal (NLC) data is a challenging task due to the multidimensional and multivariate nature of the data. Simulation study of an NLC consists of multiple timesteps, where each timestep computes scalar, vector, and tensor parameters on a geometrical mesh. Scientists developing an understanding of liquid crystal interaction and physics require tools and techniques for effective exploration, visualization, and analysis of these data sets. Traditionally, scientists have used a combination of different tools and techniques like 2D plots, histograms, cut views, etc. for data visualization and analysis. However, such an environment does not provide the required insight into NLC datasets. This thesis addresses two areas of the study of NLC data---understanding of the tensor order field (the Q-tensor) and defect detection in this field. Tensor field understanding is enhanced by using a new glyph (NLCGlyph) based on a new design metric which is closely related to the underlying physical properties of an NLC, described using the Q-tensor. A new defect detection algorithm for 3D unstructured grids based on the orientation change of the director is developed. This method has been used successfully in detecting defects for both structured and unstructured models with varying grid complexity.

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Nowadays by increasing importance of information in all sectors, illustrating data in a communicative format helps decision makers to understand and analyze effectively large amount of information in a short time. Information visualization, as a way of presenting different data types in a more understandable form, is growing increasingly in various areas. This thesis aims to investigate how information visualization could be used to increase readability and usability of transportation simulation data. Most of the previous studies in this area have focused on visualization of transportation infrastructures such as roads, bridges, etc.; while the main focus of this thesis is visualizing the outputs of transportation simulation systems. In order to study the role of information visualization in the transportation systems, we have investigated the visualization applications in a real implemented agent-based transportation simulator called TAPAS as case. In our case study, the visualization related requirements of users were analyzed, and the visualization tool was designed and developed based on the identified requirements.