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Indexed-Points Parallel Coordinates visualization of Multivariate Correlations

IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2018年第6期24卷 1997-2010页

作者： Zhou, Liang Weiskopf, Daniel Univ Stuttgart Visualizat Res Ctr VISUS Allmandring 19 D-70567 Stuttgart Germany

We address the problem of visualizing multivariate correlations in parallel coordinates. We focus on multivariate correlation in the form of linear relationships between multiple variables. Traditional parallel coordinates are well prepared to show negative correlations between two attributes by distinct visual patterns. However, it is difficult to recognize positive correlations in parallel coordinates. Furthermore, there is no support to high light multivariate correlations in parallel coordinates. In this paper, we exploit the indexed point representation of p-flats (planes in multidimensional data) to visualize local multivariate correlations in parallel coordinates. Our method yields clear visual signatures for negative and positive correlations alike, and it supports large datasets. All information is shown in a unified parallel coordinates framework, which leads to easy and familiar user interactions for analysts who have experience with traditional parallel coordinates. The usefulness of our method is demonstrated through examples of typical multidimensional datasets.

关键词： multidimensional data visualization multivariate correlations parallel coordinates

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dataopsy: Scalable and Fluid Visual Exploration using Aggregate Query Sculpting

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2024年第1期30卷 186-196页

作者： Hoque, Md Naimul Elmqvist, Niklas Univ Maryland College Pk MD 20742 USA Aarhus Univ Aarhus Denmark

We present aggregate query sculpting (AQS), a faceted visual query technique for large-scale multidimensional data. As a "born scalable" query technique, AQS starts visualization with a single visual mark representing an aggregation of the entire dataset. The user can then progressively explore the dataset through a sequence of operations abbreviated as P6: pivot (facet an aggregate based on an attribute), partition (lay out a facet in space), peek (see inside a subset using an aggregate visual representation), pile (merge two or more subsets), project (extracting a subset into a new substrate), and prune (discard an aggregate not currently of interest). We validate AQS with dataopsy, a prototype implementation of AQS that has been designed for fluid interaction on desktop and touch-based mobile devices. We demonstrate AQS and dataopsy using two case studies and three application examples.

关键词： multidimensional data visualization multivariate graphs visual queries visual exploration

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HiperView: real-time monitoring of dynamic behaviors of high-performance computing centers

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JOURNAL OF SUPERCOMPUTING 2021年第10期77卷 11807-11826页

作者： Dang, Tommy Nguyen, Ngan Chen, Yong Texas Tech Univ Lubbock TX 79409 USA

This paper presents HiperView, a visual analytics framework monitoring and characterizing the health status of high-performance computing systems through a RESTful interface in real time. The primary objectives of this visual analytical system are: (1) to provide a graphical interface for tracking the health status of a large number of data center hosts in real-time statistics, (2) to help users visually analyze unusual behavior of a series of events that may have temporal and spatial correlation, and (3) to assist in performing preliminary troubleshooting and maintenance with a visual layout that reflects the actual physical locations. Two use cases were analyzed in detail to assess the effectiveness of the HiperView on a medium-scale, Redfish-enabled production high-performance computing system with a total of 10 racks and 467 hosts. The visualization apparatus has been proven to offer the necessary support for system automation and control. Our framework's visual components and interfaces are designed to potentially handle a larger-scale data center of thousands of hosts with hundreds of various health services per host.

关键词： High-performance computing data center RESTful API Nagios Core Redfish Baseboard Management Controller (BMC) HPC visualization Radar charts Scatterplot Visual features Heatmap Boxplots Time-series data analysis multidimensional data visualization

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A novel virtual node approach for interactive visual analytics of big datasets in parallel coordinates

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2016年 55卷 510-523页

作者： Huang, Mao Lin Huang, Tze-Haw Zhang, Xuyun Tianjin Univ Sch Comp Software China 92 Weijin Rd Tianjin 300072 Peoples R China Univ Technol Sydney Sch Software FEIT POB 123 Broadway NSW 2007 Australia

Big data is a collection of large and complex datasets that commonly appear in multidimensional and multivariate data formats. It has been recognized as a big challenge in modern computing/information sciences to gain (or find out) due to its massive volume and complexity (e.g. its multivariate format). Accordingly, there is an urgent need to find new and effective techniques to deal with such huge datasets. Parallel coordinates is a well-established geometrical system for visualizing multidimensional data that has been extensively studied for decades. There is also a variety of associated interaction techniques currently used with this geometrical system. However, none of these existing techniques can achieve the functions that are covered by the Select layer of Yi's Seven-Layer Interaction Model. This is because it is theoretically impossible to find a select of data items via a mouse-click (or mouse-rollover) operation over a particular visual poly-line (a visual object) with no geometric region. In this paper, we present a novel technique that uses a set of virtual nodes to practically achieve the Select interaction which has hitherto proven to be such a challenging sphere in parallel coordinates visualization. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Big data Visual analytics Parallel coordinates Hierarchical clustering multidimensional data visualization data retrieval

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The Generalized Sensitivity Scatterplot

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2013年第10期19卷 1768-1781页

作者： Chan, Yu-Hsuan Correa, Carlos D. Ma, Kwan-Liu Univ Calif Davis Dept Comp Sci Davis CA 95616 USA

Scatterplots remain a powerful tool to visualize multidimensional data. However, accurately understanding the shape of multidimensional points from 2D projections remains challenging due to overlap. Consequently, there are a lot of variations on the scatterplot as a visual metaphor for this limitation. An important aspect often overlooked in scatterplots is the issue of sensitivity or local trend, which may help in identifying the type of relationship between two variables. However, it is not well known how or what factors influence the perception of trends from 2D scatterplots. To shed light on this aspect, we conducted an experiment where we asked people to directly draw the perceived trends on a 2D scatterplot. We found that augmenting scatterplots with local sensitivity helps to fill the gaps in visual perception while retaining the simplicity and readability of a 2D scatterplot. We call this augmentation the generalized sensitivity scatterplot (GSS). In a GSS, sensitivity coefficients are visually depicted as flow lines, which give a sense of continuity and orientation of the data that provide cues about the way data points are scattered in a higher dimensional space. We introduce a series of glyphs and operations that facilitate the analysis of multidimensional data sets using GSS, and validate with a number of well-known data sets for both regression and classification tasks.

关键词： Sensitivity analysis data transformations model fitting multidimensional data visualization

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Regression Cube: A Technique for multidimensional Visual Exploration and Interactive Pattern Finding

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ACM TRANSACTIONS ON INTERACTIVE INTELLIGENT SYSTEMS 2014年第1期4卷 1-32页

作者： Chan, Yu-Hsuan Correa, Carlos D. Ma, Kwan-Liu Univ Calif Davis Davis CA 95616 USA Lawrence Livermore Natl Lab Ctr Appl Sci Comp Livermore CA 94550 USA

Scatterplots are commonly used to visualize multidimensional data;however, 2D projections of data offer limited understanding of the high-dimensional interactions between data points. We introduce an interactive 3D extension of scatterplots called the Regression Cube (RC), which augments a 3D scatterplot with three facets on which the correlations between the two variables are revealed by sensitivity lines and sensitivity streamlines. The sensitivity visualization of local regression on the 2D projections provides insights about the shape of the data through its orientation and continuity cues. We also introduce a series of visual operations such as clustering, brushing, and selection supported in RC. By iteratively refining the selection of data points of interest, RC is able to reveal salient local correlation patterns that may otherwise remain hidden with a global analysis. We have demonstrated our system with two examples and a user-oriented evaluation, and we show how RCs enable interactive visual exploration of multidimensional datasets via a variety of classification and information retrieval tasks. A video demo of RC is available.

关键词： visualization scatterplot sensitivity analysis interactions pattern discovery data transformation model fitting multidimensional data visualization

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Two axes re-ordering methods in parallel coordinates plots

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JOURNAL OF VISUAL LANGUAGES AND COMPUTING 2016年第Apr.期33卷 3-12页

作者： Lu, Liang Fu Huang, Mao Lin Zhang, Jinson Tianjin Univ Dept Math Tianjin 300072 Peoples R China Univ Technol Sydney Fac Engn & IT Sydney NSW 2007 Australia Tianjin Univ Sch Comp Software Tianjin 300072 Peoples R China

visualization and interaction of multidimensional data are challenges in visual data analytics, which requires optimized solutions to integrate the display, exploration and analytical reasoning of data into one visual pipeline for human-centered data analysis and interpretation. Even though it is considered to be one of the most popular techniques for visualization and analysis of multidimensional data, parallel coordinate visualization is also suffered from the visual clutter problem as well as the computational complexity problem, same as other visualization methods in which visual clutter occurs where the volume of data needs to be visualized to be increasing. One straightforward way to address these problems is to change the ordering of axis to reach the minimal number of visual clutters. However, the optimization of the ordering of axes is actually a NP-complete problem. In this paper, two axes re-ordering methods are proposed in parallel coordinates visualization: (1) a contribution-based method and (2) a similarity-based method. The contribution-based re-ordering method is mainly based on the singular value decomposition (SVD) algorithm. It can not only provide users with the mathmetical theory for the selection of the first remarkable axis, but also help with visualizing detailed structure of the data according to the contribution of each data dimension. This approach reduces the computational complexity greatly in comparison with other re-ordering methods. A similarity-based re-ordering method is based on the combination of nonlinear correlation coefficient (NCC) and SVD algorithms. By using this approach, axes are reordered in line with the degree of similarities among them. It is much more rational, exact and systemic than other re-ordering methods, including those based on Pearson's correlation coefficient (PCC). Meanwhile, the paper also proposes a measurement of contribution rate of each dimension to reveal the property hidden in the dataset. At last,

关键词： multidimensional data visualization Visual analytics Parallel coordinates Axes re-ordering Singular value decomposition Nonlinear correlation coefficient

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Toward a taxonomy for 2D non-paired General Line Coordinates: a comprehensive survey

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INTERNATIONAL JOURNAL OF data SCIENCE AND ANALYTICS 2023年第2期15卷 133-158页

作者： Antonini, Antonella S. Luque, Leandro Lujan Ganuza, Maria Castro, Silvia M. Inst Comp Sci & Engn Bahia Blanca Buenos Aires Argentina

multidimensional data visualization is one of the primary foundations supporting data analysis used for understanding the hidden relationships between items and dimensions of complex data. The line-based visualization techniques are a fundamental class of multidimensional visualization techniques and cover an important set of methods that are relevant to the visual exploratory analysis. Recently, General Line Coordinates (GLCs) were introduced. These are losslessly line-based visualization techniques for multidimensional data. Particular cases of GLCs are the non-paired GLCs, which generalize the radial and parallel coordinates and have proved to be highly suitable for visualizing multidimensional data. In this context, we conduct a systematic paper review of the 2D non-paired GLC (2D-NP-GLC) visualization techniques present in the literature. We organize the 2D-NP-GLC contributions in a unified reference framework in which both the representations and the associated interactions are considered. Focusing jointly on these two criteria, we provide a useful common space for the design and development of 2D-NP-GLC techniques. Besides, this framework integrates the 2D-NP-GLC contributions and helps to identify under-explored areas that may be candidates for further research.

关键词： Visual analysis multidimensional data visualization Interactions in visualization General Line Coordinates

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Hierarchical pixel bar charts

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2002年第3期8卷 255-269页

作者： Keim, DA Hao, MC Dayal, U AT&T Corp Res Florham Pk NJ USA Univ Constance D-7750 Constance Germany Hewlett Packard Res Labs Palo Alto CA USA

Simple presentation graphics are intuitive and easy-to-use, but only show highly aggregated data. Bar charts, for example, only show a rather small number of data values and x-y-plots often have a high degree of overlap. Presentation techniques are often chosen depending on the considered data type-bar charts, for example, are used for categorical data and x-y plots are used for numerical data. In this article, we propose a combination of traditional bar charts and x-y-plots, which allows the visualization of large amounts of data with categorical and numerical data. The categorical data dimensions are used for the partitioning into the bars and the numerical data dimensions are used for the ordering arrangement within the bars. The basic idea is to use the pixels within the bars to present the detailed information of the data records. Our so-called pixel bar charts retain the intuitiveness of traditional bar charts while applying the principle of x-y charts within the bars. In many applications, a natural hierarchy is defined on the categorical data dimensions such as time, region, or product type. In hierarchical pixel bar charts, the hierarchy is exploited to split the bars for selected portions of the hierarchy. Our application to a number of real-world e-business and Web services data sets shows the wide applicability and usefulness of our new idea.

关键词： information visualization multidimensional data visualization visual data exploration and data mining very large multiattributes data sets hierarchical visualization

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JobViewer: Graph-based visualization for Monitoring High-Performance Computing System 9

JobViewer: Graph-based Visualization for Monitoring High-Per...

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IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) / 9th International Conference on Big data Computing, Applications and Technologies (BDCAT)

作者： Dang, Tommy Nguyen, Ngan V. T. Li, Jie Sill, Alan Hass, Jon Chen, Yong Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA Texas Tech Univ Ctr High Performance Comp Lubbock TX 79409 USA Dell Inc Austin TX USA

ISBN: (纸本)9781665460903

visualization aims to strengthen data exploration and analysis, especially for complex and high-dimensional data. High-performance computing (HPC) systems are typically large and complicated instruments that generate massive performance and operation time series. Monitoring HPC systems' performance is a daunting task for HPC admins and researchers due to their dynamic natures. This work proposes a visual design using the bi-partite graph's idea to visualize HPC clusters' structure, metrics, and job scheduling data. We built a web-based prototype, called JobViewer, that integrates advanced methods in visualization and human-computer interaction (HCI) to demonstrate the benefits of visualization in real-time monitoring HPC centers. We also showed real use cases and a user study to validate the efficiency and highlight the current approach's drawbacks.

关键词： High-Performance Computing Center Job Scheduling Radar Charts Time-Series data Analysis multidimensional data visualization

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