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Design Study of LineSets, a Novel Set visualization Technique

IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2011年第12期17卷 2259-2267页

作者： Alper, Basak Riche, Nathalie Henry Ramos, Gonzalo Czerwinski, Mary UC Santa Barbara Santa Barbara CA USA

Computing and visualizing sets of elements and their relationships is one of the most common tasks one performs when analyzing and organizing large amounts of data. Common representations of sets such as convex or concave geometries can become cluttered and difficult to parse when these sets overlap in multiple or complex ways, e. g., when multiple elements belong to multiple sets. In this paper, we present a design study of a novel set visual representation, LineSets, consisting of a curve connecting all of the set's elements. Our approach to design the visualization differs from traditional methodology used by the InfoVis community. We first explored the potential of the visualization concept by running a controlled experiment comparing our design sketches to results from the state-of-the-art technique. Our results demonstrated that LineSets are advantageous for certain tasks when compared to concave shapes. We discuss an implementation of LineSets based on simple heuristics and present a study demonstrating that our generated curves do as well as human-drawn ones. Finally, we present two applications of our technique in the context of search tasks on a map and community analysis tasks in social networks.

关键词： Set visualization clustering faceted data visualization graph visualization

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TreeNetViz: Revealing Patterns of Networks over Tree Structures

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2011年第12期17卷 2449-2458页

作者： Gou, Liang Zhang, Xiaolong (Luke) Penn State Univ Coll Informat Sci & Technol University Pk PA 16802 USA

Network data often contain important attributes from various dimensions such as social affiliations and areas of expertise in a social network. If such attributes exhibit a tree structure, visualizing a compound graph consisting of tree and network structures becomes complicated. How to visually reveal patterns of a network over a tree has not been fully studied. In this paper, we propose a compound graph model, TreeNet, to support visualization and analysis of a network at multiple levels of aggregation over a tree. We also present a visualization design, TreeNetViz, to offer the multiscale and cross-scale exploration and interaction of a TreeNet graph. TreeNetViz uses a Radial, Space-Filling (RSF) visualization to represent the tree structure, a circle layout with novel optimization to show aggregated networks derived from TreeNet, and an edge bundling technique to reduce visual complexity. Our circular layout algorithm reduces both total edge-crossings and edge length and also considers hierarchical structure constraints and edge weight in a TreeNet graph. These experiments illustrate that the algorithm can reduce visual cluttering in TreeNet graphs. Our case study also shows that TreeNetViz has the potential to support the analysis of a compound graph by revealing multiscale and cross-scale network patterns.

关键词： Compound graph network and tree TreeNetViz visualization multiscale and cross-scale

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Feature-Based Statistical analysis of Combustion Simulation data

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2011年第12期17卷 1822-1831页

作者： Bennett, Janine C. Krishnamoorthy, Vaidyanathan Liu, Shusen Grout, Ray W. Hawkes, Evatt R. Chen, Jacqueline H. Shepherd, Jason Pascucci, Valerio Bremer, Peer-Timo Sandia Natl Labs Livermore CA 94550 USA Univ Utah Sci Comp & Imaging Inst Salt Lake City UT 84112 USA Univ New S Wales Sydney NSW 2052 Australia Lawrence Livermore Natl Lab Livermore CA 94550 USA

We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing and reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for combustion sc

关键词： Topology Statistics data analysis data exploration visualization in Physical Sciences and Engineering Multi-variate data

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Interactive Multiscale Tensor Reconstruction for Multiresolution Volume visualization

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2011年第12期17卷 2135-2143页

作者： Suter, Susanne K. Guitian, Jose A. Iglesias Marton, Fabio Agus, Marco Elsener, Andreas Zollikofer, Christoph P. E. Gopi, M. Gobbetti, Enrico Pajarola, Renato Univ Zurich CH-8006 Zurich Switzerland Univ Calif Irvine Irvine CA USA

Large scale and structurally complex volume datasets from high-resolution 3D imaging devices or computational simulations pose a number of technical challenges for interactive visual analysis. In this paper, we present the first integration of a multiscale volume representation based on tensor approximation within a GPU-accelerated out-of-core multiresolution rendering framework. Specific contributions include (a) a hierarchical brick-tensor decomposition approach for pre-processing large volume data, (b) a GPU accelerated tensor reconstruction implementation exploiting CUDA capabilities, and (c) an effective tensor-specific quantization strategy for reducing data transfer bandwidth and out-of-core memory footprint. Our multiscale representation allows for the extraction, analysis and display of structural features at variable spatial scales, while adaptive level-of-detail rendering methods make it possible to interactively explore large datasets within a constrained memory footprint. The quality and performance of our prototype system is evaluated on large structurally complex datasets, including gigabyte-sized micro-tomographic volumes.

关键词： GPU/CUDA multiscale tensor reconstruction interactive volume visualization multiresolution rendering

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A Task Based Performance Evaluation of visualization Approaches for Categorical data analysis

A Task Based Performance Evaluation of Visualization Approac...

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15th International conference on Information Visualisation (IV)

作者： Fernstad, Sara Johansson Johansson, Jimmy Linkoping Univ C Res Linkoping Sweden

ISBN: (纸本)9780769544762

Categorical data is common within many areas and efficient methods for analysis are needed. It is, however, often difficult to analyse categorical data since no general measure of similarity exists. One approach is to represent the categories with numerical values (quantification) prior to visualization using methods for numerical data. Another is to use visual representations specifically designed for categorical data. Although commonly used, very little guidance is available as to which method may be most useful for different analysis tasks. This paper presents an evaluation comparing the performance of employing quantification prior to visualization and visualization using a method designed for categorical data. It also provides a guidance as to which visualization approach is most useful in the context of two basic data analysis tasks: one related to similarity structures and one related to category frequency. The results strongly indicate that the quantification approach is most efficient for the similarity related task, whereas the visual representation designed for categorical data is most efficient for the task related to category frequency.

关键词： Categorical data Quantitative Evaluation Usability Studies Parallel Sets Quantification

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细胞功能的可视化及信息整合分析--第69次“双清论坛”综述

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中国科学基金 2012年第4期26卷 195-198,204页

作者：张智红吕群燕骆清铭程京曹雪涛华中科技大学-武汉光电国家实验室(筹) Britton chance生物医学光子学研究中心武汉430074 国家自然科学基金委员会医学科学部北京100085 清华大学生物医学工程系生物芯片北京国家工程研究中心北京100084 中国医学科学院北京100730

20世纪生物学家由体外获得的大量生物分子和细胞的功能信息,亟待通过活体内动态研究加以证实。近年来迅速发展的分子成像技术使得细胞功能的可视化与信息整合研究成为可能。国家自然科学基金委员会于2011年9月16—18日,在北京召开了主题... 详细信息

20世纪生物学家由体外获得的大量生物分子和细胞的功能信息,亟待通过活体内动态研究加以证实。近年来迅速发展的分子成像技术使得细胞功能的可视化与信息整合研究成为可能。国家自然科学基金委员会于2011年9月16—18日,在北京召开了主题为"细胞功能的可视化及信息整合分析"的第69期"双清论坛"学术研讨会。论坛围绕生理与病理状态宿主细胞功能的动态定量可视化及相关信息学研究的现状、前沿热点问题、发展趋势以及相关新技术新仪器的研发等多方面展开研讨。同时,结合我国的研究现状,分析我国在该领域的研究基础与优势,凝练和提出我国在这方面急需关注和解决的重要基础科学问题,以及今后5—10年的重点资助方向。来自国内外12所高校和科研院所的免疫学、信息学、医学、纳米科学和化学等领域的专家、学者出席此次论坛。

关键词：细胞功能可视化成像技术信息整合双清论坛

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Interactive, Graph-Based Visual analysis of High-Dimensional, Multi-Parameter Fluorescence Microscopy data in Toponomics

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2011年第12期17卷 1882-1891页

作者： Oeltze, Steffen Freiler, Wolfgang Hillert, Reyk Doleisch, Helmut Preim, Bernhard Schubert, Walter Univ Magdeburg D-39106 Magdeburg Germany SimVis GmbH Vienna Austria

In Toponomics, the function protein pattern in cells or tissue (the toponome) is imaged and analyzed for applications in toxicology, new drug development and patient-drug-interaction. The most advanced imaging technique is robot-driven multi-parameter fluorescence microscopy. This technique is capable of co-mapping hundreds of proteins and their distribution and assembly in protein clusters across a cell or tissue sample by running cycles of fluorescence tagging with monoclonal antibodies or other affinity reagents, imaging, and bleaching in situ. The imaging results in complex multi-parameter data composed of one slice or a 3D volume per affinity reagent. Biologists are particularly interested in the localization of co-occurring proteins, the frequency of co-occurrence and the distribution of co-occurring proteins across the cell. We present an interactive visual analysis approach for the evaluation of multi-parameter fluorescence microscopy data in toponomics. Multiple, linked views facilitate the definition of features by brushing multiple dimensions. The feature specification result is linked to all views establishing a focus+context visualization in 3D. In a new attribute view, we integrate techniques from graph visualization. Each node in the graph represents an affinity reagent while each edge represents two co-occurring affinity reagent bindings. The graph visualization is enhanced by glyphs which encode specific properties of the binding. The graph view is equipped with brushing facilities. By brushing in the spatial and attribute domain, the biologist achieves a better understanding of the function protein patterns of a cell. Furthermore, an interactive table view is integrated which summarizes unique fluorescence patterns. We discuss our approach with respect to a cell probe containing lymphocytes and a prostate tissue section.

关键词： Visual Analytics Fluorescence Microscopy Toponomics Protein Interaction Graph visualization

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Prisma Maps - A Geovisualization Support for Prisma Multiple Coordinated View Information visualization Tool

Prisma Maps - A Geovisualization Support for Prisma Multiple...

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15th International conference on Information Visualisation (IV)

作者： Yasojima, Edson Koiti Kudo Meiguins, Bianchi Serique Carneiro, Nikolas Veras, Rafael Meiguins, Aruanda Simoes Fed Univ Para UFPA Belem Para Brazil Rede Informat Ltda Ctr Univ Para CESUPA Belem Para Brazil

ISBN: (纸本)9780769544762

This paper presents a new view for PRISMA information visualization tool;this new visualization will provide support for analyzing data on maps. The use of maps will provide a way for geographical data analysis by using coordinates. Geographic analysis becomes important when it comes to verification of database that uses the location as a key factor to create contexts. This paper will describe the new integrated view and main features of the map.

关键词： Information visualization Geographical analysis Map View

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Interactive Animated visualizations of Breast, Ovarian Cancer and Other Health Indicator data using Weave, an Interactive Web-based analysis and visualization Environment

Interactive Animated Visualizations of Breast, Ovarian Cance...

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15th International conference on Information Visualisation (IV)

作者： Purushe, Shweta Grinstein, Georges Smrtic, Mary Beth Lyons, Helen Univ Massachusetts Dept Comp Sci Inst Visualizat & Percept Res Lowell MA 01854 USA

ISBN: (纸本)9780769544762

Recent research in genomics and biomedical studies has shown a relationship between heredity, mutations in certain genes and the corresponding probabilities of developing certain cancers. In this study we looked at breast and ovarian cancer distributions across all states and counties in the United States over time. We describe briefly Weave, our Web-based analysis and visualization Environment and use it to explore these cancers and present interactive animated visualizations of family hereditary patterns and genetic distributions, not only for these cancers, but also for other life-threatening cancers and related health indicators. We also show how Weave can be used to integrate other diverse epidemiological data, in particular obesity, and explore its relationship with cancer data.

关键词： visualization

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Multidimensional visualization Techniques for Microarray data

Multidimensional Visualization Techniques for Microarray Dat...

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15th International conference on Information Visualisation (IV)

作者： Cvek, Urska Trutschl, Marjan Kilgore, Phillip C. Stone, Randolph, II Clifford, John L. LSU Shreveport Dept Comp Sci Shreveport LA 71103 USA LSU Hlth Sci Ctr Ctr Mol & Tumor Virol Shreveport LA USA LSU Hlth Sci Ctr Dept Biochem Shreveport LA USA

ISBN: (纸本)9780769544762

analysis of high-dimensional microarray expression data is based mostly on the statistical approaches that are indispensable for the study of biological systems. To aid the analysis and exploration of such data, the process of analyzing such data is often enhanced with visual, data mining and other computational techniques. We utilize a set of tools for the visual analysis of data aimed at generating the hypotheses. We show the usability of classic and novel multi-dimensional visualization tools in life sciences. Additionally, we survey and show a few multidimensional visualization tools applied to the process of data exploration using a urothelial cell carcinoma of the bladder time course. These tools have the potential of uncovering non-trivial relationships and structures in the data.

关键词： information visualization multidimensional data self-organizing maps

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