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Real-Time Visual Analytics for Text Streams

COMPUTER 2013年第7期46卷 47-55页

作者： Keim, Daniel A. Krstajic, Milos Rohrdantz, Christian Schreck, Tobias Univ Konstanz Dept Comp Sci Constance Germany Univ Konstanz Visualizat & Data Anal Grp Constance Germany Univ Konstanz Dept Comp & Informat Sci Constance Germany

Combining automated analysis and visual-interactive displays helps analysts rapidly sort through volumes of raw text to detect critical events and identify surrounding issues.

关键词： Visual Analytics data visualization Knowledge Discovery Real Time Systems interactive Systems Real Time data Analysis Visual Analytics Knowledge Discovery interactive data visualization

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interactive simulation and visualization

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COMPUTER 1999年第12期32卷 59-+页

作者： Johnson, C Parker, SG Hansen, C Kindlmann, GL Livnat, Y Univ Utah Ctr Sci Comp & Imaging Salt Lake City UT 84112 USA Univ Utah Dept Comp Sci Salt Lake City UT 84112 USA

Most researchers who perform data analysis and visualization do so only after everything else is finished, which often means that they don't discover errors invalidating the results of their simulation until postprocessing. A better approach would be to improve the integration of simulation and visualization into the entire process so that they can make adjustments along the way. This approach, called computational steering, is the capacity to control all aspects of the computational science pipeline. Recently, several tools and environments for computational steering have begun to emerge. These tools range from those that modify an application's performance characteristics, either by automated means or by user interaction, to those that modify the underlying computational application. A refined problem-solving environment should facilitate everything from algorithm development to application steering. The authors discuss some tools that provide a mechanism to integrate modeling, simulation, data analysis, and visualization.

关键词： algorithm development application performance characteristics application steering computational science computational steering data analysis data analysis data visualisation digital simulation interactive data visualization interactive simulation interactive systems modelling refined problem-solving environment

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Visual exploration of data by using multidimensional scaling on multicore CPU, GPU, and MPI cluster

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2014年第3期26卷 662-682页

作者： Pawliczek, Piotr Dzwinel, Witold Yuen, David A. Univ Texas Houston Med Sch Houston Dept Biochem & Mol Biol Houston TX 77030 USA AGH Univ Sci & Technol Dept Comp Sci Krakow Poland Univ Minnesota Dept Minnesota Supercomp Inst Minneapolis MN 55455 USA AGH Univ Sci & Technol Fac Met Engn & Ind Comp Sci Dept Appl Comp Sci & Modeling Krakow Poland

Visual and interactive data exploration requires fast and reliable tools for embedding of an original data space in 3(2)-dimensional Euclidean space. Multidimensional scaling (MDS) is a good candidate. However, owing to at least O(M-2) memory and time complexity, MDS is computationally demanding for interactive visualization of data sets consisting of order of 10(4) objects on computer systems, ranging from PC with multicore CPU processor, graphics processing unit (GPU) board to midrange MPI clusters. To explore interactively data sets of that size, we have developed novel efficient parallel algorithms for MDS mapping based on virtual particle dynamics. We demonstrate that the performance of our MDS algorithms implemented in compute unified device architecture environment on a PC equipped with a modern GPU board (Tesla M2090, GeForce GTX 480) is considerably faster than its MPI/OpenMP parallel implementation on the modern midrange professional cluster (10 nodes, each equipped with 2x Intel Xeon X5670 CPUs). We also show that the hybridized two-level MPI/CUDA implementation, run on a cluster of GPU nodes, can additionally provide a linear speedup. Copyright 2013 John Wiley & Sons, Ltd.

关键词： data mining interactive data visualization multidimensional scaling method of particles multicore CPU GPU-CUDA MPI cluster

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The practical use of an interactive visualization and planning tool for intervention planning in particle accelerator environments with ionizing radiation

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2014年第0期743卷 14-21页

作者： Fabry, Thomas Blaha, Jan Vanherpe, Liesbeth Braesch, Christian Tabourot, Laurent Feral, Bruno CERN European Org Nucl Res CH-1211 Geneva 23 Switzerland Univ Savoie SYMME Polytech Annecy Chambery F-74944 Annecy Le Vieux France

A core issue during the planning of a maintenance intervention in a facility with ionizing radiation is the minimization of the integrated equivalent dose contracted by the maintenance workers during the intervention. In this work, we explore the use of a technical-scientific software program facilitating the intervention planning in irradiated environments using sound mathematical concepts. We show how the software can be used in planning future operations using a case studies: the decommissioning of a beam dump for a linear 160 MeV H- accelerator. interactive visualization of the facilities and radiation levels, as well as tools for interactive trajectory planning are explored, as well as automatic calculation of the expected integrated individual dose contracted during an intervention. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Ionizing radiation Intervention planning Radiation dose minimization interactive data visualization FLUKA

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Globe Browsing in OpenSpace

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COMPUTER 2018年第5期51卷 8-9页

作者： De Floriani, Leila Univ Maryland College Pk MD 20742 USA

This installment of Computer's series highlighting the work published in IEEE Computer Society journals comes from IEEE Transactions on visualization and Computer Graphics.

关键词： Spotlight On Transactions History Of Computing interactive data visualization Planetary Surface visualization Open Space Scientific Computing

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Visual Analysis of Social Media data

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COMPUTER 2013年第5期46卷 68-75页

作者： Schreck, Tobias Keim, Daniel Univ Konstanz Dept Comp & Informat Sci Constance Germany Univ Konstanz Dept Comp & Informat Sci Visualizat & Data Anal Grp Constance Germany

The application of visual analytics, which combines the advantages of computational knowledge discovery and interactive visualization, to social media data highlights the many benefits of this integrated approach.

关键词： Media data visualization Visual Analytics data Analysis data Mining Social Network Services Complex data Visual Analytics Knowledge Discovery interactive data visualization Social Media Text visualization

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Elaborator: A Novel App for Insights into Laboratory data of Clinical Trials

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THERAPEUTIC INNOVATION & REGULATORY SCIENCE 2021年第6期55卷 1220-1229页

作者： Janitza, Silke Majumder, Madhurima Mendolia, Franco Jeske, Steffen Kulmann, Hermann Bayer AG Res & Dev Pharmaceut Stat & Data Insights D-13342 Berlin Germany Bayer US LLC Whippany NJ USA Bayer AG Wuppertal Germany Chrestos Concept GmbH & Co KG Essen Germany

In clinical studies there are huge numbers of laboratory parameters available that are measured at several visits for several treatment groups. The status quo for presenting laboratory data in clinical trials consists in generating large numbers of tables and data listings. Such tables and listings are required for submissions to health authorities. However, reviewing laboratory data presented in the form of tables and listings is a lengthy and tedious process. Thus, to enable efficient exploration of laboratory data we developed elaborator, a comprehensive and easy-to-use interactive browser-based application. The elaborator app comprises three analyses types for addressing different questions, for example about changes in laboratory values that frequently occur, treatment-related changes and changes beyond the normal ranges. In this way, the app can be used by study teams for identifying safety signals in a clinical trial as well as for generating hypotheses that are further inspected with detailed analyses and possibly data from other sources. The elaborator app is implemented in the statistical software R. The R package elaborator can be obtained from https://***/package=elaborator. Patients' laboratory data need to be extracted from the clinical database and pre-processed locally for feeding into the app. For exploring data by means of the elaborator, the user needs some familiarity with R but no programming knowledge is required.

关键词： interactive data visualization Question-based visualization data exploration Clinical review Clinical trial Clinical laboratory data

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AdEPro: Animation of Adverse Event Profiles-Presentation of an Easy-to-Use App for Visually Exploring Individual Study data

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THERAPEUTIC INNOVATION & REGULATORY SCIENCE 2020年第6期54卷 1512-1521页

作者： Mentenich, Nicole Tasto, Christoph Becker, Bastian Bayer AG Stat & Data Insights D-42113 Wuppertal Germany

Motivation Reviewing the adverse event data collected in clinical trials is a lengthy and tedious process when these data are presented in the form of tables, data listings, and static graphs. Thus, to enable anyone interested in exploring adverse event data efficiently and relatively independently, we developed AdEPro, a compact, powerful, and easy-to-use interactive app. Description and Use of the App AdEPro is an app for (audio-)visualizing adverse event data from clinical trials. The app dynamically displays the onset, severity, and development of selected adverse events on the individual subject level and on the treatment group level. This paper illustrates that there are numerous questions related to adverse events that can be approached by means of AdEPro, e.g., questions about temporal aspects of adverse events, associations between adverse events, and the influence of subject characteristics. AdEPro provides quick first answers to such questions;however, it does not provide statistical proof. Essentially, it acts as a versatile "hypothesis generator," helping the user to decide whether further analyses are indicated. No programming knowledge is required for exploring data by means of AdEPro. However, the user needs some basic knowledge of the software R and of extracting data from a clinical data base. The software code is open source, allowing modifications and expansions of the app, if desired. Availability and Implementation AdEPro can be freely obtained from. It runs on any computer on which R is installed. Patient data are stored and processed locally.

关键词： interactive data visualization Question-based visualization data exploration Clinical review Clinical trial

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Mosaic plots

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WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS 2012年第2期4卷 191-198页

作者： Theus, Martin Univ Augsburg Dept Comp Oriented Stat & Data Anal Augsburg Germany

Mosaic plots are a recursive generalization of barcharts to visualize multivariate categorical data. Mosaic plots were first introduced by Hartigan and Kleiner and are the de-facto standard for advanced visualizations of categorical data. This review explains the general construction and the successful application of mosaic plots. Recent implementations of mosaic plots offer various generalizations which can effectively cope with almost any kind of categorical data. Besides plotting expected values of a proposed model, these variations include the doubledecker plot, the empty bin display and fluctuation diagrams. Within an interactive graphical analysis tool, subgroups reflecting a certain property can easily be linked into mosaic plots to effectively visualize the relationship between this property and the categorical variables in the plot. Mosaic plots are most powerful when investigating associations between categorical variables, conditioned by other categorical variables. This is illustrated along with an explanation of how Simpson's Paradox for categorical data can be explained using mosaic plots. (C) 2012 Wiley Periodicals, Inc.

关键词： categorical data multivariate analysis high-dimension graphics contingency tables interactive data visualization

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NYenviroScreen: An open-source data driven method for identifying potential environmental justice communities in New York State

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ENVIRONMENTAL SCIENCE & POLICY 2021年 124卷 348-358页

作者： Petroni, Michael Howard, Sarah Howell, I. Brielle Collins, Mary B. SUNY Coll Environm Sci & Forestry Ctr Environm Med & Informat 1 Forestry Dr Syracuse NY 13210 USA SUNY Coll Environm Sci & Forestry Grad Program Environm Sci 1 Forestry Dr Syracuse NY 13210 USA SUNY Coll Environm Sci & Forestry Dept Environm Studies 1 Forestry Dr Syracuse NY 13210 USA

In 2003, the New York State Department of Environmental Conservation began designating Potential Environmental Justice Areas (PEJA) for the purpose of providing public participation opportunities to disadvantaged communities during permitting deliberations. We developed NYenviroScreen to help stakeholders understand, review, and provide input for how future PEJA designation might be updated and improved, including for identifying disadvantaged communities under the newly enacted Climate Leadership and Community Protection Act (CLCPA). We present and compare three potential update methods and provide an interactive web application for investigating model components and composition. The three methods are: (i) three factor clustering using the Jenks natural breaks algorithm, (ii) a cumulative impact model adapted from CalEPA's CalEnviroScreen, and (iii) a hybrid approach that uses both methods and incorporates Federal and State recognized tribal land areas. NYenviroScreen brings together federal and state data sources related to population health, sociodemographics, environmental risk factors, and potential pollution exposures for 15,463 census block groups. We find that a hybrid approach provides the most robust coverage for both rural and urban areas of New York State. This publicly accessible innovative approach is an important, data driven effort toward the pursuit of environmental justice in New York State.

关键词： Actionable science Environmental justice Environmental policy interactive data visualization Cumulative risk screening

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