820 research outputs found

    Direct Multifield Volume Ray Casting of Fiber Surfaces

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    Multifield data are common in visualization. However, reducing these data to comprehensible geometry is a challenging problem. Fiber surfaces, an analogy of isosurfaces to bivariate volume data, are a promising new mechanism for understanding multifield volumes. In this work, we explore direct ray casting of fiber surfaces from volume data without any explicit geometry extraction. We sample directly along rays in domain space, and perform geometric tests in range space where fibers are defined, using a signed distance field derived from the control polygons. Our method requires little preprocess, and enables real-time exploration of data, dynamic modification and pixel-exact rendering of fiber surfaces, and support for higher-order interpolation in domain space. We demonstrate this approach on several bivariate datasets, including analysis of multi-field combustion data

    TOWARDS EFFICIENT PRESENTATION AND INTERACTION IN VISUAL DATA ANALYSIS

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    The "data explosion'' since the era of the Internet has increased data size tremendously, from several hundred Megabytes to millions of Terabytes. Large amounts of data may not fit into memory, and a proper way of handling and processing the data is necessary. Besides, analyses of such large scale data requires complex and time consuming algorithms. On the other hand, humans play an important role in steering and driving the data analysis, while there are often times when people have a hard time getting an overview of the data or knowing which analysis to run. Sometimes they may not even know where to start. There is a huge gap between the data and understanding. An intuitive way to facilitate data analysis is to visualize it. Visualization is understandable and illustrative, while using it to support fast and rapid data exploration of large scale datasets has been a challenge for a long time. In this dissertation, we aim to facilitate efficient visual data exploration of large scale datasets from two perspectives: efficiency and interaction. The former indicates how users could understand the data efficiently, this depends on various factors, such as how fast data is processed and how data is presented, while the latter focuses more on the users: how they deal with the data and why they interact with the system in a particular way. In order to improve the efficiency of data exploration, we have looked into two steps in the visualization pipeline: rendering and processing (computations). We first address visualization rendering of large dataset through a thorough evaluation of web-based visualization performance. We evaluate and understand the page loading effects of Scalable Vector Graphics (SVG), a popular image format for interactive visualization on the web browsers. To understand the scalability of individual elements in SVG based visualization, we conduct performance tests on different types of charts, in different phases of rendering process. From the results, we have figured out optimization techniques and guidelines to achieve better performance when rendering SVG visualization. Secondly, we present a pure browser based distributed computing framework (VisHive) that exploits computational power from co-located idle devices for visualization. The VisHive framework speeds up web-based visualization, which is originally designed for single computer and cannot make use of additional computational resources on the client side. It takes advantage of multiple devices that today's users often have access to. VisHive constructs visualization applications that can transparently connect multiple devices into an ad-hoc cluster for local computation. It requires no specific software to be downloaded for setup. To achieve a more interactive data analysis process, we first propose a proactive visual analytics system (DataSite) that enable users to analyze the data smoothly with a list of pre-defined algorithms. DataSite provides results through selecting and executing computations using automatic server-side computation. It utilizes computational resources exhaustively during data analysis to reduce the burden of human thinking. Analyzing results identified by these background processes are surfaced as status updates in a feed on the front-end, akin to posts in a social media feed. DataSite effectively turns data analysis into a conversation between the user and the computer, thereby reducing the cognitive load and domain knowledge requirements on users. Next we apply the concept of proactive data analysis to genomic data, and explore how to improve data analysis through adaptive computations in bioinformatics domain. We build Epiviz Feed, a web application that supports proactive visual and statistical analysis of genomic data. It addresses common and popular biological questions that may be asked by the analyst, and shortens the time of processing and analyzing the data with automatic computations. We further present a computational steering mechanism for visual analytics that prioritizes computations performed on the dataset leveraging the analyst's navigational behavior in the data. The web-based system, called Sherpa, provides computational modules for genomic data analysis, where independent algorithms calculate test statistics relevant to biological inferences about gene regulation in various tumor types and their corresponding normal tissues

    Doctor of Philosophy

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    dissertationCorrelation is a powerful relationship measure used in many fields to estimate trends and make forecasts. When the data are complex, large, and high dimensional, correlation identification is challenging. Several visualization methods have been proposed to solve these problems, but they all have limitations in accuracy, speed, or scalability. In this dissertation, we propose a methodology that provides new visual designs that show details when possible and aggregates when necessary, along with robust interactive mechanisms that together enable quick identification and investigation of meaningful relationships in large and high-dimensional data. We propose four techniques using this methodology. Depending on data size and dimensionality, the most appropriate visualization technique can be provided to optimize the analysis performance. First, to improve correlation identification tasks between two dimensions, we propose a new correlation task-specific visualization method called correlation coordinate plot (CCP). CCP transforms data into a powerful coordinate system for estimating the direction and strength of correlations among dimensions. Next, we propose three visualization designs to optimize correlation identification tasks in large and multidimensional data. The first is snowflake visualization (Snowflake), a focus+context layout for exploring all pairwise correlations. The next proposed design is a new interactive design for representing and exploring data relationships in parallel coordinate plots (PCPs) for large data, called data scalable parallel coordinate plots (DSPCP). Finally, we propose a novel technique for storing and accessing the multiway dependencies through visualization (MultiDepViz). We evaluate these approaches by using various use cases, compare them to prior work, and generate user studies to demonstrate how our proposed approaches help users explore correlation in large data efficiently. Our results confirmed that CCP/Snowflake, DSPCP, and MultiDepViz methods outperform some current visualization techniques such as scatterplots (SCPs), PCPs, SCP matrix, Corrgram, Angular Histogram, and UntangleMap in both accuracy and timing. Finally, these approaches are applied in real-world applications such as a debugging tool, large-scale code performance data, and large-scale climate data

    Scalable multi-class sampling via filtered sliced optimal transport

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    We propose a multi-class point optimization formulation based on continuous Wasserstein barycenters. Our formulation is designed to handle hundreds to thousands of optimization objectives and comes with a practical optimization scheme. We demonstrate the effectiveness of our framework on various sampling applications like stippling, object placement, and Monte-Carlo integration. We a derive multi-class error bound for perceptual rendering error which can be minimized using our optimization. We provide source code at https://github.com/iribis/filtered-sliced-optimal-transport.Comment: 15 pages, 17 figures, ACM Trans. Graph., Vol. 41, No. 6, Article 261. Publication date: December 202

    Adaptive multiresolution visualization of large multidimensional multivariate scientific datasets

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    The sizes of today\u27s scientific datasets range from megabytes to terabytes, making it impossible to directly browse the raw datasets visually. This presents significant challenges for visualization scientists who are interested in supporting these datasets. In this thesis, we present an adaptive data representation model which can be utilized with many of the commonly employed visualization techniques when dealing with large amounts of data. Our hierarchical design also alleviates the long standing visualization problem due to limited display space. The idea is based on using compactly supported orthogonal wavelets and additional downsizing techniques to generate a hierarchy of fine to coarse approximations of a very large dataset for visualization. An adaptive data hierarchy, which contains authentic multiresolution approximations and the corresponding error, has many advantages over the original data. First, it allows scientists to visualize the overall structure of a dataset by browsing its coarse approximations. Second, the fine approximations of the hierarchy provide local details of the interesting data subsets. Third, the error of the data representation can provide the scientist with information about the authenticity of the data approximation. Finally, in a client-server network environment, a coarse representation can increase the efficiency of a visualization process by quickly giving users a rough idea of the dataset before they decide whether to continue the transmission or to abort it. For datasets which require long rendering time, an authentic approximation of a very large dataset can speed up the visualization process greatly. Variations on the main wavelet-based multiresolution hierarchy described in this thesis also lead to other multiresolution representation mechanisms. For example, we investigate the uses of norm projections and principal components to build multiresolution data hierarchies of large multivariate datasets. This leads to the development of a more flexible dual multiresolution visualization environment for large data exploration. We present the results of experimental studies of our adaptive multiresolution representation using wavelets. Utilizing a multiresolution data hierarchy, we illustrate that information access from a dataset with tens of millions of data values can be achieved in real time. Based on these results, we propose procedures to assist in generating a multiresolution hierarchy of a large dataset. For example, the findings indicate that an ordinary computed tomography volume dataset can be represented effectively for some tasks by an adaptive data hierarchy with less than 1.5% of its original size

    Revisiting the Design Patterns of Composite Visualizations

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    Composite visualization is a popular design strategy that represents complex datasets by integrating multiple visualizations in a meaningful and aesthetic layout, such as juxtaposition, overlay, and nesting. With this strategy, numerous novel designs have been proposed in visualization publications to accomplish various visual analytic tasks. These well-crafted composite visualizations have formed a valuable collection for designers and researchers to address real-world problems and inspire new research topics and designs. However, there is a lack of understanding of design patterns of composite visualization, thus failing to provide holistic design space and concrete examples for practical use. In this paper, we opted to revisit the composite visualizations in VIS publications and answered what and how visualizations of different types are composed together. To achieve this, we first constructed a corpus of composite visualizations from IEEE VIS publications and decomposed them into a series of basic visualization types (e.g., bar chart, map, and matrix). With this corpus, we studied the spatial (e.g., separated or overlaying) and semantic relationships (e.g., with same types or shared axis) between visualizations and proposed a taxonomy consisting of eight different design patterns (e.g., repeated, stacked, accompanied, and nested). Furthermore, we analyzed and discussed common practices of composite visualizations, such as the distribution of different patterns and correlations between visualization types. From the analysis and examples, we obtained insights into different design patterns on the utilities, advantages, and disadvantages. Finally, we developed an interactive system to help visualization developers and researchers conveniently explore collected examples and design patterns

    Doctor of Philosophy

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    dissertationWith the ever-increasing amount of available computing resources and sensing devices, a wide variety of high-dimensional datasets are being produced in numerous fields. The complexity and increasing popularity of these data have led to new challenges and opportunities in visualization. Since most display devices are limited to communication through two-dimensional (2D) images, many visualization methods rely on 2D projections to express high-dimensional information. Such a reduction of dimension leads to an explosion in the number of 2D representations required to visualize high-dimensional spaces, each giving a glimpse of the high-dimensional information. As a result, one of the most important challenges in visualizing high-dimensional datasets is the automatic filtration and summarization of the large exploration space consisting of all 2D projections. In this dissertation, a new type of algorithm is introduced to reduce the exploration space that identifies a small set of projections that capture the intrinsic structure of high-dimensional data. In addition, a general framework for summarizing the structure of quality measures in the space of all linear 2D projections is presented. However, identifying the representative or informative projections is only part of the challenge. Due to the high-dimensional nature of these datasets, obtaining insights and arriving at conclusions based solely on 2D representations are limited and prone to error. How to interpret the inaccuracies and resolve the ambiguity in the 2D projections is the other half of the puzzle. This dissertation introduces projection distortion error measures and interactive manipulation schemes that allow the understanding of high-dimensional structures via data manipulation in 2D projections
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