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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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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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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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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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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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visualization Rhetoric: Framing Effects in Narrative visualization

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2011年第12期17卷 2231-2240页

作者： Hullman, Jessica Diakopoulos, Nicholas Univ Michigan Ann Arbor MI 48109 USA Rutgers State Univ Piscataway NJ 08855 USA

Narrative visualizations combine conventions of communicative and exploratory information visualization to convey an intended story. We demonstrate visualization rhetoric as an analytical framework for understanding how design techniques that prioritize particular interpretations in visualizations that "tell a story" can significantly affect end-user interpretation. We draw a parallel between narrative visualization interpretation and evidence from framing studies in political messaging, decision-making, and literary studies. Devices for understanding the rhetorical nature of narrative information visualizations are presented, informed by the rigorous application of concepts from critical theory, semiotics, journalism, and political theory. We draw attention to how design tactics represent additions or omissions of information at various levels-the data, visual representation, textual annotations, and interactivity-and how visualizations denote and connote phenomena with reference to unstated viewing conventions and codes. Classes of rhetorical techniques identified via a systematic analysis of recent narrative visualizations are presented, and characterized according to their rhetorical contribution to the visualization. We describe how designers and researchers can benefit from the potentially positive aspects of visualization rhetoric in designing engaging, layered narrative visualizations and how our framework can shed light on how a visualization design prioritizes specific interpretations. We identify areas where future inquiry into visualization rhetoric can improve understanding of visualization interpretation.

关键词： Rhetoric narrative visualization framing effects semiotics denotation connotation

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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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BirdVis: Visualizing and Understanding Bird Populations

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2011年第12期17卷 2374-2383页

作者： Ferreira, Nivan Lins, Lauro Fink, Daniel Kelling, Steve Wood, Chris Freire, Juliana Silva, Claudio NYU Polytech Inst New York NY 10003 USA Cornell Univ Cornell Lab Ithaca NY 14853 USA

Birds are unrivaled windows into biotic processes at all levels and are proven indicators of ecological well-being. Understanding the determinants of species distributions and their dynamics is an important aspect of ecology and is critical for conservation and management. Through crowdsourcing, since 2002, the eBird project has been collecting bird observation records. These observations, together with local-scale environmental covariates such as climate, habitat, and vegetation phenology have been a valuable resource for a global community of educators, land managers, ornithologists, and conservation biologists. By associating environmental inputs with observed patterns of bird occurrence, predictive models have been developed that provide a statistical framework to harness available data for predicting species distributions and making inferences about species-habitat associations. Understanding these models, however, is challenging because they require scientists to quantify and compare multiscale spatial-temporal patterns. A large series of coordinated or sequential plots must be generated, individually programmed, and manually composed for analysis. This hampers the exploration and is a barrier to making the cross-species comparisons that are essential for coordinating conservation and extracting important ecological information. To address these limitations, as part of a collaboration among computer scientists, statisticians, biologists and ornithologists, we have developed BirdVis, an interactive visualization system that supports the analysis of spatio-temporal bird distribution models. BirdVis leverages visualization techniques and uses them in a novel way to better assist users in the exploration of interdependencies among model parameters. Furthermore, the system allows for comparative visualization through coordinated views, providing an intuitive interface to identify relevant correlations and patterns. We justify our design decisions and present case s

关键词： Ornithology species distribution models multiscale analysis spatial data temporal data

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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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Privacy-Preserving data visualization using Parallel Coordinates

Privacy-Preserving Data Visualization using Parallel Coordin...

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conference on visualization and data analysis 2011

作者： Dasgupta, Aritra Kosara, Robert Univ N Carolina Charlotte NC 28223 USA

ISBN: (纸本)9780819484055

The proliferation of data in the past decade has created demand for innovative tools in different areas of exploratory data analysis, like data mining and information visualization. However, the problem with real-world datasets is that many of their attributes can identify individuals, or the data are proprietary and valuable. The field of data mining has developed a variety of ways for dealing with such data, and has established an entire subfield for privacy-preserving data mining. visualization, on the other hand, has seen little, if any, work on handling sensitive data. With the growing applicability of data visualization in real-world scenarios, the handling of sensitive data has become a non-trivial issue we need to address in developing visualization tools. With this goal in mind, in this paper, we analyze the issue of privacy from a visualization perspective and propose a privacy-preserving visualization technique based on clustering in parallel coordinates. We also outline the key differences in approach from the privacy-preserving data mining field and compare the advantages and drawbacks of our approach.

关键词： privacy k-anonymity visualization parallel coordinates

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