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Parallel sets: Interactive exploration and visual analysis of categorical data

ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2006年第4期12卷 558-568页

作者： Kosara, R Bendix, F Hauser, H Univ N Carolina Dept Comp Sci Charlotte NC 28223 USA VRVis Res Ctr A-1220 Vienna Austria

Categorical data dimensions appear in many real-world data sets, but few visualization methods exist that properly deal with them. Parallel Sets are a new method for the visualization and interactive exploration of categorical data that shows data frequencies instead of the individual data points. The method is based on the axis layout of parallel coordinates, with boxes representing the categories and parallelograms between the axes showing the relations between categories. In addition to the visual representation, we designed a rich set of interactions. Parallel Sets allow the user to interactively remap the data to new categorizations and, thus, to consider more data dimensions during exploration and analysis than usually possible. At the same time, a metalevel, semantic representation of the data is built. Common procedures, like building the cross product of two or more dimensions, can be performed automatically, thus complementing the interactive visualization. We demonstrate Parallel Sets by analyzing a large CRM data set, as well as investigating housing data from two US states.

关键词： information visualization interaction nominal data categorical data multivariate data

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Tracking Features in Embedded Surfaces: Understanding Extinction in Turbulent Combustion 5

Tracking Features in Embedded Surfaces: Understanding Extinc...

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ieee 5th symposium on large data analysis and visualization (LDAV)

作者： Widanagamaachchi, Wathsala Klacansky, Pavol Kolla, Hemanth Bhagatwala, Ankit Chen, Jackie Pascucci, Valerio Bremer, Peer-Timo Univ Utah SCI Inst Salt Lake City UT 84112 USA Sandia Natl Labs Albuquerque NM USA Lawrence Livermore Natl Lab Livermore CA USA

ISBN: (纸本)9781467385176

Understanding the temporal evolution of features of interest requires the ability to: (i) extract features from each snapshot;(ii) correlate them over time;and (iii) understand the resulting tracking graph. This paper provides new solutions for the last two challenges in the context of large-scale turbulent combustion simulations. In particular, we present a simple and general algorithm to correlate hierarchical features, embedded in time-dependent surfaces. This, for the first time, provides a parameter independent approach to track embedded features. Furthermore, we provide a new technique to adaptively change feature parameters over time to both: alleviate artifacts due to insufficient temporal resolution as well as to simplify the resulting tracking graphs to promote new scientific insights. Our solutions are integrated into a general and flexible analysis environment that allows users to interactively explore the spatio-temporal behavior of large-scale simulations. We demonstrate the results using the analysis of extinction holes in turbulent combustion as primary case study and a number of other applications to illustrate the generality of the approach.

关键词： E.1 [data STRUCTURES]: Graphs and Networks J.2 [PHYSICAL SCIENCES AND ENGINEERING]: Engineering turbulent combustion Extinction EXTINCTION temporal resolution embedding tracking

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10 Years of MegaMol The Pain and Gain of Creating Your Own visualization Framework

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ieee COMPUTER GRAPHICS AND APPLICATIONS 2018年第1期38卷 109-114页

作者： Krone, Michael Grottel, Sebastian Reina, Guido Muller, Christoph Ertl, Thomas Univ Stuttgart Visualizat Res Ctr Stuttgart Germany Tech Univ Dresden Comp Graph & Visualizat Lab Dresden Germany Univ Stuttgart Inst Visualizat & Interact Syst Stuttgart Germany

This article discusses our experience in creating MegaMol, an open-source visualization framework for large particle-based data.

关键词： applications computer graphics data visualization framework Graphics processing units megamol Programming Prototypes Rendering (computer graphics) Task analysis visualization

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Interactive visual analysis of the NSF funding information

Interactive visual analysis of the NSF funding information

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ieee Pacific Visualisation symposium

作者： Liu, Shixia Cao, Nan Lv, Hao IBM China Research Lab Shanghai Jiaotong University IBM China Research Lab

ISBN: (纸本)9781424419661

This paper presents an interactive visualization toolkit for navigating and analyzing the National Science Foundation (NSF) funding information. Our design builds upon the treemap layout and the stacked graph to contribute customized techniques for visually navigating and interacting with the hierarchical data of NSF programs and proposals, supporting visual search and analysis, and allowing the user to make informed decision. In this visualization toolkit, we propose two visualization techniques to simplify the navigation of the hierarchical data: 2.5 Dimensional treemaps to make the hierarchical structure more easily to be recognized, and labeled treemap to help the user to get a clear overview of the content of the structure and make the internal area of rectangles correspond to the weights of the data set. Furthermore, an incremental layout method is adopted to handle information on a large scale. The improved treemap visualization will help to visually analyze the static funding data and the stacked graph is utilized to analyze the time-series data. Through these visual analysis techniques, research trends of NSF, popular NSF programs are quickly identified. The primary contribution is a demonstration of novel ways to effectively present and analyze NSF funding data.

关键词： Cascaded rectangles Interactive visualization Overview+detail Time series data visualization Treemaps

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DNS Firewall data visualization

DNS Firewall Data Visualization

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IFIP/ieee symposium on Integrated Network and Service Management (IM)

作者： Spacek, Stanislav Rusnak, Vit Dombajova, Anna-Marie Masaryk Univ Inst Comp Sci Brno Czech Republic Masaryk Univ Fac Informat Brno Czech Republic

ISBN: (纸本)9783903176157

Common security tools generate a lot of data suitable for further analysis. However, the raw form of the data is often too complex and useful information gets lost in a large volume of records. In this paper, we propose a system for visualization of the data generated by a DNS firewall and outline a process of visually emphasizing information important to incident handlers. Our prototype suggests that such visualization is possible, keeping the balance between the amount of displayed information and the level of detail.

关键词： data visualization

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Trelliscope: A system for detailed visualization in the deep analysis of large complex data

Trelliscope: A system for detailed visualization in the deep...

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2013 3rd ieee symposium on large-Scale data analysis and visualization, LDAV 2013

作者： Hafen, Ryan Gosink, Luke McDermott, Jason Rodland, Karin Dam, Kerstin Kleese-Van Cleveland, William S. Pacific Northwest National Lab United States Purdue University United States

ISBN: (纸本)9781479916597

Trelliscope emanates from the Trellis Display framework for visualization and the Divide and Recombine (D&R) approach to analyzing large complex data. In Trellis, the data are broken up into subsets, a visualization method is applied to each subset, and the display result is an array of panels, one per subset. This is a powerful framework for visualization of data, both small and large. In D&R, the data are broken up into subsets, and any analytic method from statistics and machine learning is applied to each subset independently. Then the outputs are recombined. This provides not only a powerful framework for analysis, but also feasible and practical computations using distributed computational facilities. It enables deep analysis of the data: study of both data summaries as well as the detailed data at their finest granularity. This is critical to full understanding of the data. It also enables the analyst to program using an interactive high-level language for data analysis such as R, which allows the analyst to focus more on the data and less on code. In this paper we introduce Trelliscope, a system that scales Trellis to large complex data. It provides a way to create displays with a very large number of panels and an interactive viewer that allows the analyst to sort, filter, and sample the panels in a meaningful way. We discuss the underlying principles, design, and scalable architecture of Trelliscope, and illustrate its use on three analysis projects in proteomics, high intensity physics, and power systems engineering. © 2013 ieee.

关键词： visualization

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Beamtrees: Compact visualization of large hierarchies

Beamtrees: Compact visualization of large hierarchies

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ieee symposium on Information visualization (INFOVIS 2002)

作者： van Ham, F van Wijk, JJ Tech Univ Eindhoven Dept Math & Comp Sci NL-5600 MB Eindhoven Netherlands

ISBN: (纸本)076951751X

Beamtrees are a new method for the visualization of large hierarchical data sets. Nodes are shown as stacked circular beams, such that both the hierarchical structure as well as the size of nodes are depicted. The dimensions of beams are calculated using a variation of the treemap algorithm. A small user study indicated that beamtrees are significantly more effective than nested treemaps and cushion treemaps for the extraction of global hierarchical information.

关键词： Computer science data mining data visualization Mathematics Testing Three dimensional displays

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A four-level focus plus context approach to interactive visual analysis of temporal features in large scientific data

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COMPUTER GRAPHICS FORUM 2008年第3期27卷 775-782页

作者： Muigg, Philipp Kehrer, Johannes Oeltze, Steffen Piringer, Harald Doleisch, Helmut Preim, Bernhard Hauser, Helwig VRVis Res Ctr Vienna Austria Univ Magdeburg Dept Simulat & Graph D-39106 Magdeburg Germany Univ Bergen Dept Informat N-5008 Bergen Norway

In this paper we present a new approach to the interactive visual analysis of time-dependent scientific data both from measurements as well as from computational simulation-by visualizing a scalar function. over time for each of tenthousands or even millions of sample points. In order to cope with overdrawing and cluttering, we introduce a new four-level method of focus+context visualization. Based on a setting of coordinated, multiple views (with linking and brushing), we integrate three different kinds of focus and also the context in every single view. Per data item we use three values (front the unit interval each) to represent to which degree the data item is part of the respective focus level. We present a color compositing scheme which is capable of expressing all three values in a meaningful way, taking semantics and their relations amongst each other (in the context of our multiple linked view setup) into account. Furthermore, we present additional image-based postprocessing methods to enhance the visualization of large sets of function graphs, including a texture-based technique based on line integral convolution (LIC). We also propose advanced brushing techniques which are specific to the time-dependent nature of the data (in order to brush patterns over time more efficiently). We demonstrate the usefulness of the new approach in the context of medical perfusion data.

关键词： Simulation methods & models Visual analytics Scalar field theory Information visualization Semantics Digital image processing Convolutions (Mathematics)

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Geometric Quantification of Features in large Flow Fields

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ieee COMPUTER GRAPHICS AND APPLICATIONS 2012年第4期32卷 46-54页

作者： Kendall, Wesley Huang, Jian Peterka, Tom Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Argonne Natl Lab Math & Comp Sci Div Argonne IL USA

Interactive exploration of flow features in large-scale 3D unsteady-flow data is one of the most challenging visualization problems today. To comprehensively explore the complex feature spaces in these datasets, a proposed system employs a scalable framework for investigating a multitude of characteristics from traced field lines. This capability supports the examination of various neighborhood-based geometric attributes in concert with other scalar quantities. Such an analysis wasn't previously possible because of the large computational overhead and I/O requirements. The system integrates visual analytics methods by letting users procedurally and interactively describe and extract high-level flow features. An exploration of various phenomena in a large global ocean-modeling simulation demonstrates the approach's generality and expressiveness as well as its efficacy. [ABSTRACT FROM AUTHOR]

关键词： computer graphics Feature extraction geometric flow analysis Geometry large-scale data analysis large-scale systems parallel I/O particle tracing Three dimensional displays Visual analytics visualization

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Parallel sets: Visual analysis of categorical data

Parallel sets: Visual analysis of categorical data

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ieee symposium on Information visualization (InfoVis 05)

作者： Bendix, F Kosara, R Hauser, H VRVis Research Center Austria Inte:Ligand

ISBN: (纸本)078039464X

The discrete nature of categorical data makes it a particular challenge for visualization. Methods that work very well for continuous data are often hardly usable with categorical dimensions. Only few methods deal properly with such data, mostly because of the discrete nature of categorical data, which does not translate well into the continuous domains of space and color. Parallel Sets is a new visualization method that adopts the layout of parallel coordinates, but substitutes the individual data points by a frequency-based representation. This abstracted view, combined with a set of carefully designed interactions, supports visual data analysis of large and complex data sets. The technique allows efficient work with meta data, which is particularly important when dealing with categorical datasets. By creating new dimensions from existing ones, for example, the user can filter the data according to his or her current needs. We also present the results from an interactive analysis of CRM data using Parallel Sets. We demonstrate how the flexible layout eases the process of knowledge crystallization, especially when combined with a sophisticated interaction scheme.

关键词： categorical data meta information interaction

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