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Challenges in Visual analysis of Ensembles

ieee COMPUTER GRAPHICS AND APPLICATIONS 2018年第2期38卷 122-131页

作者： Crossno, Patricia Sandia Natl Labs Livermore CA 94550 USA

Modeling physical phenomena through computational simulation increasingly relies on generating a collection of related runs, known as an ensemble. This article explores the challenges we face in developing analysis and visualization systems for large and complex ensemble data sets, which we seek to understand without having to view the results of every simulation run. Implementing approaches and ideas developed in response to this goal, we demonstrate the analysis of a 15K run material fracturing study using Slycat, our ensemble analysis system.

关键词： data models Computational modeling Analytical models visualization data visualization Time series analysis

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Visual analysis of large Multivariate Scattered data using Clustering and Probabilistic Summaries

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2021年第2期27卷 1580-1590页

作者： Rapp, Tobias Peters, Christoph Dachsbacher, Carsten Karlsruhe Inst Technol Karlsruhe Germany

Rapidly growing data sizes of scientific simulations pose significant challenges for interactive visualization and analysis techniques. In this work, we propose a compact probabilistic representation to interactively visualize large scattered datasets. In contrast to previous approaches that represent blocks of volumetric data using probability distributions, we model clusters of arbitrarily structured multivariate data. In detail, we discuss how to efficiently represent and store a high-dimensional distribution for each cluster. We observe that it suffices to consider low-dimensional marginal distributions for two or three data dimensions at a time to employ common visual analysis techniques. Based on this observation, we represent high-dimensional distributions by combinations of low-dimensional Gaussian mixture models. We discuss the application of common interactive visual analysis techniques to this representation. In particular, we investigate several frequency-based views, such as density plots in 1D and 2D, density-based parallel coordinates, and a time histogram. We visualize the uncertainty introduced by the representation, discuss a level-of-detail mechanism, and explicitly visualize outliers. Furthermore, we propose a spatial visualization by splatting anisotropic 3D Gaussians for which we derive a closed-form solution. Lastly, we describe the application of brushing and linking to this clustered representation. Our evaluation on several large, real-world datasets demonstrates the scaling of our approach.

关键词： data visualization Probabilistic logic data models visualization Three-dimensional displays Gaussian mixture model interactive visual analysis probabilistic data summaries multivariate data scattered data Gaussian mixture models Gaussian rendering

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NodeTrix: A hybrid visualization of social networks

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2007年第6期13卷 1302-1309页

作者： Henry, Nathalie Fekete, Jean-Daniel McGuffin, Michael J. Univ Paris Sud INRIA Futurs Orsay France Univ Toronto Ontario Canc Inst Toronto ON Canada

The need to visualize large social networks is growing as hardware capabilities make analyzing large networks feasible and many new data sets become available. Unfortunately, the visualizations in existing systems do not satisfactorily resolve the basic dilemma of being readable both for the global structure of the network and also for detailed analysis of local communities. To address this problem, we present NodeTrix, a hybrid representation for networks that combines the advantages of two traditional representations: node-link diagrams are used to show the global structure of a network, while arbitrary portions of the network can be shown as adjacency matrices to better support the analysis of communities. A key contribution is a set of interaction techniques. These allow analysts to create a NodeTrix visualization by dragging selections to and from node-link and matrix forms, and to flexibly manipulate the NocleTrix representation to explore the dataset and create meaningful summary visualizations of their findings. Finally, we present a case study applying NodeTrix to the analysis of the InfoVis 2004 coauthorship clataset to illustrate the capabilities of NocleTrix as both an exploration tool and an effective means of communicating results.

关键词： network visualization matrix visualization hybrid visualization aggreiation interaction

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2023 ieee Scientific visualization Contest Winner: VisAnywhere: Developing Multiplatform Scientific visualization Applications

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ieee COMPUTER GRAPHICS AND APPLICATIONS 2024年第5期44卷 93-103页

作者： Marrinan, Thomas Mateevitsi, Victor A. Moeller, Madeleine Kanayinkal, Alina Papka, Michael E. Univ St Thomas St Paul 55105 MN USA Argonne Natl Lab Lemont IL 60439 USA

Scientists often explore and analyze large-scale scientific simulation data by leveraging 2-D and 3-D visualizations. The data and tasks can be complex and therefore best supported using myriad display technologies, from mobile devices to large high-resolution display walls to virtual reality headsets. Using a simulation of neuron connections in the human brain provided for the 2023 ieee Scientific visualization Contest, we present our work leveraging various web technologies to create a multiplatform scientific visualization application. Users can spread visualization and interaction across multiple devices to support flexible user interfaces and both colocated and remote collaboration. Drawing inspiration from responsive web design principles, this work demonstrates that a single codebase can be adapted to develop scientific visualization applications that operate everywhere.

关键词： Neurons data visualization Collaboration Three-dimensional displays Calcium Web design Task analysis

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Security in Process: Visually Supported Triage analysis in Industrial Process data

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2020年第4期26卷 1638-1649页

作者： Lohfink, Anna-Pia Anton, Simon D. Duque Schotten, Hans Dieter Leitte, Heike Garth, Christoph Univ Kaiserslautern Sci Visualizat Lab Kaiserslautern Germany German Res Ctr Artificial Intelligence Kaiserslau Kaiserslautern Germany Univ Kaiserslautern Visual Informat Anal Grp Kaiserslautern Germany

Operation technology networks, i.e. hard- and software used for monitoring and controlling physical/industrial processes, have been considered immune to cyber attacks for a long time. A recent increase of attacks in these networks proves this assumption wrong. Several technical constraints lead to approaches to detect attacks on industrial processes using available sensor data. This setting differs fundamentally from anomaly detection in IT-network traffic and requires new visualization approaches adapted to the common periodical behavior in OT-network data. We present a tailored visualization system that utilizes inherent features of measurements from industrial processes to full capacity to provide insight into the data and support triage analysis by laymen and experts. The novel combination of spiral plots with results from anomaly detection was implemented in an interactive system. The capabilities of our system are demonstrated using sensor and actuator data from a real-world water treatment process with introduced attacks. Exemplary analysis strategies are presented. Finally, we evaluate effectiveness and usability of our system and perform an expert evaluation.

关键词： Cyber Security Information visualization Anomaly Detection Triage analysis Operation Technology Networks

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visualization for Information Exploration and analysis

Visualization for Information Exploration and Analysis

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ieee symposium on Visual Languages and Human-Centric Computing

作者： Stasko, John Georgia Inst Technol Sch Interact Comp Atlanta GA 30332 USA

ISBN: (纸本)9781424425280

Making sense of data becomes more challenging as the data grows larger and becomes more complex. If a picture truly can be worth a thousand words, then clever visualizations of data should hold promise in helping people with sense-making tasks. I firmly believe that visual representations of data can help people to better explore, analyze, and understand it, thus transforming the data into information. In this talk, I will explain how visualization and visual analytics help people make sense of data and I will provide many such examples. I also will describe my present research into visualization for investigative analysis. This project explores how visual analytics can help investigators examine a large document collection in order to discover embedded stories and narratives scattered across the documents in the collection.

关键词： visualization

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Visualizing Big Energy data

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ieee POWER & ENERGY MAGAZINE 2018年第3期16卷 18-25页

作者： Hyndman, Rob J. Liu, Xueqin (Amy) Pinson, Pierre Monash Business Sch Caulfield Vic Australia Queens Univ Belfast Belfast Antrim North Ireland Tech Univ Denmark Lyngby Denmark

visualization is a crucial component of data analysis. It is always a good idea to plot the data before fitting models, making predictions, or drawing conclusions. As sensors of the electric grid are collecting large volumes of data from various sources, power industry professionals are facing the challenge of visualizing such data in a timely fashion. In this article, we demonstrate several data-visualization solutions for big energy data through three case studies involving smart-meter data, phasor measurement unit (PMU) data, and probabilistic forecasts, respectively.

关键词： Phasor measurement units data visualization data analysis Principal component analysis Smart meters data models Probability distribution Big data

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DICON: Interactive Visual analysis of Multidimensional Clusters

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2011年第12期17卷 2581-2590页

作者： Cao, Nan Gotz, David Sun, Jimeng Qu, Huamin Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Hong Kong Peoples R China IBM TJ Watson Res Ctr Hawthorne NY USA

Clustering as a fundamental data analysis technique has been widely used in many analytic applications. However, it is often difficult for users to understand and evaluate multidimensional clustering results, especially the quality of clusters and their semantics. For large and complex data, high-level statistical information about the clusters is often needed for users to evaluate cluster quality while a detailed display of multidimensional attributes of the data is necessary to understand the meaning of clusters. In this paper, we introduce DICON, an icon-based cluster visualization that embeds statistical information into a multi-attribute display to facilitate cluster interpretation, evaluation, and comparison. We design a treemap-like icon to represent a multidimensional cluster, and the quality of the cluster can be conveniently evaluated with the embedded statistical information. We further develop a novel layout algorithm which can generate similar icons for similar clusters, making comparisons of clusters easier. User interaction and clutter reduction are integrated into the system to help users more effectively analyze and refine clustering results for large datasets. We demonstrate the power of DICON through a user study and a case study in the healthcare domain. Our evaluation shows the benefits of the technique, especially in support of complex multidimensional cluster analysis.

关键词： Visual analysis Clustering Information visualization

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Utilizing Many-Core Accelerators for Halo and Center Finding within a Cosmology Simulation 5

Utilizing Many-Core Accelerators for Halo and Center Finding...

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

作者： Sewell, Christopher Lo, Li-ta Heitmann, Katrin Habib, Salman Ahrens, James Los Alamos Natl Lab Los Alamos NM 87545 USA Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9781467385176

Efficiently finding and computing statistics about "halos" (regions of high density) are essential analysis steps for N-body cosmology simulations. However, in state-of-the-art simulation codes, these analysis operators do not currently take advantage of the shared-memory data-parallelism available on multi-core and many-core architectures. The Hybrid / Hardware Accelerated Cosmology Code (HACC) is designed as an MPI+X code, but the analysis operators are parallelized only among MPI ranks, because of the difficulty in porting different X implementations (e.g., OpenMP, CUDA) across all architectures on which it is run. In this paper, we present portable data-parallel algorithms for several variations of halo finding and halo center finding algorithms. These are implemented with the PISTON component of the VTK-m framework, which uses Nvidia's Thrust library to construct data-parallel algorithms that allow a single implementation to be compiled to multiple backends to target a variety of multi-core and many-core architectures. Finally, we compare the performance of our halo and center finding algorithms against the original HACC implementations on the Moonlight, Stampede, and Titan supercomputers. The portability of Thrust allowed the same code to run efficiently on each of these architectures. On Titan, the performance improvements using our code have enabled halo analysis to be performed on a very large data set (81923 particles across 16,384 nodes of Titan) for which analysis using only the existing CPU algorithms was not feasible.

关键词： D.1.3 [Software]: Programming Techniques ??? [Concurrent Prgm.] haloes Titan ARCHITECTURE Codes Cosmology FINDINGS thrust Portability algorithms data parallel

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Voronoi-Based Extraction and visualization of Molecular Paths

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2011年第12期17卷 2025-2034页

作者： Lindow, Norbert Baum, Daniel Hege, Hans-Christian Zuse Inst Berlin Berlin Germany

Visual analysis is widely used to study the behavior of molecules. Of particular interest are the analysis of molecular interactions and the investigation of binding sites. For large molecules, however, it is difficult to detect possible binding sites and paths leading to these sites by pure visual inspection. In this paper, we present new methods for the computation and visualization of potential molecular paths. Using a novel filtering method, we extract the significant paths from the Voronoi diagram of spheres. For the interactive visualization of molecules and their paths, we present several methods using deferred shading and other state-of-the-art techniques. To allow for a fast overview of reachable regions of the molecule, we illuminate the molecular surface using a large number of light sources placed on the extracted paths. We also provide a method to compute the extension surface of selected paths and visualize it using the skin surface. Furthermore, we use the extension surface to clip the molecule to allow easy visual tracking of even deeply buried paths. The methods are applied to several proteins to demonstrate their usefulness.

关键词： Molecular visualization data filtering geometry-based techniques view-dependent visualization

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