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VAST 09 - ieee symposium on Visual Analytics Science and Technology

作者： Guo, Zhenyu Ward, Matthew O. Rundensteiner, Elke A. Computer Science Department Worcester Polytechnic Institute United States

ISBN: (纸本)9781424452835

Discovering and extracting linear trends and correlations in datasets is very important for analysts to understand multivariate phenomena. However, current widely used multivariate visualization techniques, such as parallel coordinates and scatterplot matrices, fail to reveal and illustrate such linear relationships intuitively, especially when more than 3 variables are involved or multiple trends coexist in the dataset. We present a novel multivariate model parameter space visualization system that helps analysts discover single and multiple linear patterns and extract subsets of data that fit a model well. Using this system, analysts are able to explore and navigate in model parameter space, interactively select and tune patterns, and refine the model for accuracy using computational techniques. We build connections between model space and data space visually, allowing analysts to employ their domain knowledge during exploration to better interpret the patterns they discover and their validity. Case studies with real datasets are used to investigate the effectiveness of the visualizations. ©2009 ieee.

关键词： visualization

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GPU accelerated plant growth modeling and visualization

GPU accelerated plant growth modeling and visualization

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3rd International symposium on Plant Growth Modeling, Simulation, visualization and Applications, PMA09

作者： Zhao, Xing Hu, Jingjing Kang, Mengzhen School of Mathematical Sciences Capital Normal University Beijing 100048 China School of Software Beijing Institute of Technology Beijing 100081 China Institute of Automation Chinese Academy of Sciences Beijing 100080 China

ISBN: (纸本)9780769539881

We present a GPU-accelerated algorithm for plant growth modeling and visualization. In this algorithm, plant topological structures and geometrical structures are represented separately. Plant topological structures are generated by dual-scale automaton, and geometrical structures are dynamically constructed in parallel by the geometry shader of graphics Processing Unit (GPU). This scheme greatly reduces the amount of transferred data between main memory and GPU memory, and speeds up plant growth modeling and rendering without compromising image quality. An application for cotton growth modeling and visualization is given in detail to confirm the advantages of the proposed algorithm. © 2010 ieee.

关键词： graphics processing unit

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A scalable architecture for visual data exploration

A scalable architecture for visual data exploration

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VAST 09 - ieee symposium on Visual Analytics Science and Technology

作者： Decker, Jonathan Godwin, Alex Livingston, Mark A. Royle, Denise Naval Research Laboratory United States Charles River Analytics United States

ISBN: (纸本)9781424452835

Intelligence analysts in the areas of defense and homeland security are now faced with the difficult problem of discerning the relevant details amidst massive data stores. We propose a component-based visualization architecture that is built specifically to encourage the flexible exploration of geospatial event databases. The proposed system is designed to deploy on a variety of display layouts, from a single laptop screen to a multi-monitor tiled-display. By utilizing a combination of parallel coordinates, principal components plots, and other data views, analysts may reduce the dimensionality of a data set to its most salient features. Of particular value to our target applications are understanding correlations between data layers, both within a single view and across multiple views. Our proposed system aims to address the limited scalability associated with coordinated multiple views (CMVs) through the implementation of an efficient core application which is extensible by the end-user. ©2009 ieee.

关键词： visualization

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Combining automated analysis and visualization techniques for effective exploration of high-dimensional data

Combining automated analysis and visualization techniques fo...

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VAST 09 - ieee symposium on Visual Analytics Science and Technology

作者： Tatu, Andrada Albuquerque, Georgia Eisemann, Martin Schneidewind, Jörn Theisel, Holger Magnork, Marcus Keim, Daniel University of Konstanz Germany TU Braunschweig Germany Telefonica o2 Business Intelligence Center Germany University of Magdeburg Germany

ISBN: (纸本)9781424452835

Visual exploration of multivariate data typically requires projection onto lower-dimensional representations. The number of possible representations grows rapidly with the number of dimensions, and manual exploration quickly becomes ineffective or even unfeasible. This paper proposes automatic analysis methods to extract potentially relevant visual structures from a set of candidate visualizations. Based on features, the visualizations are ranked in accordance with a specified user task. The user is provided with a manageable number of potentially useful candidate visualizations, which can be used as a starting point for interactive data analysis. This can effectively ease the task of finding truly useful visualizations and potentially speed up the data exploration task. In this paper, we present ranking measures for class-based as well as non class-based Scatterplots and parallel Coordinates visualizations. The proposed analysis methods are evaluated on different datasets. ©2009 ieee.

关键词： visualization

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Guided analysis of hurricane trends using statistical processes integrated with interactive parallel coordinates

Guided analysis of hurricane trends using statistical proces...

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VAST 09 - ieee symposium on Visual Analytics Science and Technology

作者： Steed, Chad A. Swan II, J. Edward Jankun-Kelly, T.J. Fitzpatrick, Patrick J. Naval Research Laboratory United States Mississippi State University United States

ISBN: (纸本)9781424452835

This paper demonstrates the promise of augmenting interactive multivariate representations with information from statistical processes in the domain of weather data analysis. Statistical regression, correlation analysis, and descriptive statistical calculations are integrated via graphical indicators into an enhanced parallel coordinates system, called the Multidimensional Data eXplorer (MDX). These statistical indicators, which highlight significant associations in the data, are complemented with interactive visual analysis capabilities. The resulting system allows a smooth, interactive, and highly visual workflow. The system's utility is demonstrated with an extensive hurricane climate study that was conducted by a hurricane expert. In the study, the expert used a new data set of environmental weather data, composed of 28 independent variables, to predict annual hurricane activity. MDX shows the Atlantic Meridional Mode increases the explained variance of hurricane seasonal activity by 7-15% and removes less significant variables used in earlier studies. The findings and feedback from the expert (1) validate the utility of the data set for hurricane prediction, and (2) indicate that the integration of statistical processes with interactive parallel coordinates, as implemented in MDX, addresses both deficiencies in traditional weather data analysis and exhibits some of the expected benefits of visual data analysis. ©2009 ieee.

关键词： Hurricanes

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Biomedical imaging ecosystem and the role of the GPU

Biomedical imaging ecosystem and the role of the GPU

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ieee International symposium on Biomedical Imaging

作者： Kimberly Powell NVIDIA Corporation USA

ISBN: (纸本)9781424439317

The biomedical imaging chain is continuously being challenged to reconstruct, analyze, and visualize increasing amounts of data in shorter amounts of time. parallel computing on multi-core devices and clustered computers has allowed for continued innovation of compute and processing technologies but not without facing serious constraints of cost, space, and power consumption. Over the last three years the graphics processing unit (GPU) and its increased programmability has played an integral role in defining a new dimension to parallel computing with its single chip, many-core architecture as well as evolving the graphics pipeline to enhance visualization techniques. Image reconstruction, segmentation and registration algorithms architected to take advantage of the GPU parallel architecture not only realize massive processing speedups but also set the stage for scalability. High resolution rendering of 3D and 4D datasets are navigated in interactive, real-time approaches. Real time ray tracing and 3D stereoscopic solutions bring increased realism to images. Understanding the optimized mix of GPU and CPU, both in the sense of hardware and software, is necessary for imaging applications to innovate, realize cost/performance efficiency and continue to enhance visualization. Several approaches for GPU programmability are available and will be explored. Innovations in the compute, graphics and visualization space will be discussed to show the relevance of the GPU throughout the imaging chain.

关键词： Biomedical imaging Ecosystems visualization Image reconstruction parallel processing Biomedical computing Concurrent computing Technological innovation Space technology graphics

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MultiClusterTree: Interactive Visual Exploration of Hierarchical Clusters in Multidimensional Multivariate Data

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COMPUTER graphics FORUM 2009年第3期28卷 823-830页

作者： Van Long, Tran Linsen, Lars Jacobs Univ Sch Sci & Engn Bremen Germany

Visual analytics of multidimensional multivariate data is a challenging task because of the difficulty in understanding metrics in attribute spaces with more than three dimensions. Frequently, the analysis goal is not to look into individual records but to understand the distribution of the records at large and to find clusters of records with similar attribute values. A large number of (typically hierarchical) clustering algorithms have been developed to group individual records to clusters of statistical significance. However, only few visualization techniques exist for further exploring and understanding the clustering results. We propose visualization and interaction methods for analyzing individual clusters as well as cluster distribution within and across levels in the cluster hierarchy We also provide a clustering method that operates on density rather than individual records. To not restrict our search for clusters, we compute density in the given multidimensional multivariate space. Clusters are formed by areas of high density We present an approach that automatically computes a hierarchical tree of high density, clusters. To visually represent the cluster hierarchy, we present a 2D radial layout that supports an intuitive understanding of the distribution structure of the multidimensional multivariate data set. Individual clusters can be explored interactively using parallel coordinates when being selected in the cluster tree. Furthermore, we integrate circular parallel coordinates into the radial hierarchical cluster tree layout, which allows for the analysis of the overall cluster distribution. This visual representation supports the comprehension of the relations between clusters and the original attributes. The combination of the 2D radial layout and the circular parallel coordinates is used to overcome the overplotting problem of parallel coordinates when looking into data sets with many records. We apply an automatic coloring scheme based on the 2D r

关键词： Computer graphics [I.3.3]: Picture/Image Generation—Display Algorithms

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Splatting the Lines in parallel Coordinates

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COMPUTER graphics FORUM 2009年第3期28卷 759-766页

作者： Zhou, Hong Cui, Weiwei Qu, Huamin Wu, Yingcai Yuan, Xiaoru Zhuo, Wei Hong Kong Univ Sci & Technol Hong Kong Hong Kong Peoples R China Peking Univ Sch EECS Beijing Peoples R China Peking Univ Key Lab Machine Percept Minist Educ Beijing Peoples R China

In this paper, we propose a novel splatting framework for clutter reduction and pattern revealing in parallel coordinates. Our framework consists of two major components: a polyline splatter for cluster detection and a segment splatter for clutter reduction. The cluster detection is performed by splatting the lines one by one into the parallel coordinates plots, and for each splatted line we enhance its neighboring lines and suppress irrelevant ones. To reduce visual clutter caused by line crossings and overlappings in the clustered results, we provide a segment splatter which represents each polyline by one segment and splats these segments with different speeds, colors, and lengths from the leftmost axis to the rightmost axis. Users can interactively control both the polyline splatting and the segment splatting processes to emphasize the features they are interested in. The experimental results demonstrate that our framework can effectively reveal some hidden patterns in parallel coordinates.

关键词： Clutter (information theory)

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SpRay: A visual analytics approach for gene expression data

SpRay: A visual analytics approach for gene expression data

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ieee symposium on Visual Analytics Science and Technology (VAST)

作者： Janko Dietzsch Julian Heinrich Kay Nieselt Dirk Bartz ZBIT University of Tübingen Germany University of Stuttgart Germany ICCAS/VCM Universität Leipzig Germany

We present a new application, SpRay, designed for the visual exploration of gene expression data. It is based on an extension and adaption of parallel coordinates to support the visual exploration of large and high-dimensional datasets. In particular, we investigate the visual analysis of gene expression data as generated by micro-array experiments; We combine refined visual exploration with statistical methods to a visual analytics approach that proved to be particularly successful in this application domain. We will demonstrate the usefulness on several multidimensional gene expression datasets from different bioinformatics applications.

关键词： Spraying Visual analytics Gene expression Bioinformatics Statistical analysis Application software Terminology Computer graphics Data analysis Data visualization

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Illustrative parallel coordinates

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COMPUTER graphics FORUM 2008年第3期27卷 1031-1038页

作者： McDonnell, K. T. Mueller, K. SUNY Stony Brook Ctr Visual Comp Stony Brook NY USA

Illustrative parallel coordinates (IPC) is a suite of artistic rendering techniques for augmenting and improving parallel coordinate (PC) visualizations. IPC techniques can be used to convey a large amount of information about a multidimensional dataset in a small area of the screen through the following approaches: (a) edge-bundling through splines;(b) visualization of "branched" clusters to reveal the distribution of the data;(c) opacity-based hints to show cluster density;(d) opacity and shading effects to illustrate local line density on the parallel axes;and (e) silhouettes, shadows and halos to help the eye distinguish between overlapping clusters. Thus, the primary goal of this work is to convey as much information as possible in a manner that is aesthetically pleasing and easy to understand for non-experts.

关键词： I.3.3 [Computer graphics]: Display algorithms I.3.3 [Computer graphics]: Interaction techniques

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