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Correlation Coordinate Plots: Efficient Layouts for Correlation Tasks 11

Correlation Coordinate Plots: Efficient Layouts for Correlat...

11th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP)

作者： Hoa Nguyen Rosen, Paul Univ Utah Sci Comp & Imaging Inst Salt Lake City UT 84112 USA Univ S Florida Dept Comp Sci & Engn Tampa FL USA

ISBN: (纸本)9783319648705;9783319648699

Correlation is a powerful measure of relationships assisting in estimating trends and making forecasts. It's use is widespread, being a critical data analysis component of fields including science, engineering, and business. Unfortunately, visualization methods used to identify and estimate correlation are designed to be general, supporting many visualization tasks. Due in large part to their generality, they do not provide the most efficient interface, in terms of speed and accuracy for correlation identifying. To address this shortcoming, we first propose a new correlation task-specific visual design called Correlation Coordinate Plots (CCPs). CCPs transform data into a powerful coordinate system for estimating the direction and strength of correlation. To extend the functionality of this approach to multiple attribute datasets, we propose two approaches. The first design is the Snowflake visualization, a focus+context layout for exploring all pairwise correlations. The second design enhances the CCP by using principal component analysis to project multiple attributes. We validate CCP by applying it to real-world data sets and test its performance in correlation-specific tasks through an extensive user study that showed improvement in both accuracy and speed of correlation identification.

关键词： Correlation identification Correlation visualization multidimensional data visualization

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Parallel Coordinates Plotting as a Method in Process Control Hazard Identification

Parallel Coordinates Plotting as a Method in Process Control...

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IEEE Systems and Information Engineering Design Symposium (SIEDS)

作者： Comfort, James R. Warner, Thomas R. Vargo, Erik P. Bass, Ellen J. Univ Virginia Syst & Informat Engn Dept Charlottesville VA 22903 USA

ISBN: (纸本)9781457704475

Abnormal events in process plants waste billions of dollars annually. Taking advantage of plant operators' perceptual capabilities in the hazard identification process could enhance the aiding provided by other decision support systems. Parallel coordinate plotting allows the visualization of multidimensional data by making apparent geometric patterns that exist in the data sets. This work investigates the use of parallel coordinate plotting in anomalous process control applications. Approaches utilizing the software packages CVE and CPM as decision support tools are explored. These approaches with CVE and CPM are carried out in a case study on historical plant processing data. CVE is used to identify normal plant operating conditions and construct a "best operating zone" that can be loaded into CPM. CPM can then read in real-time or historical data and indicate (i.e, generate alarms) if the plant is leaving normal operating conditions and also recommend mitigation strategies to return the plant to normal operating conditions. CPM's alarms are evaluated as an effective method for determining the onset of a hazard event. Advantages and limitations of CVE and CPM as a decision support are identified through the case study. The results are discussed in the context of the static case study and the need for simulations is highlighted.

关键词： CPM alarm CVE data visualization Decision support systems Hazards Process control Real time systems Software packages data visualisation decision support system decision support systems hazards historical plant processing data industrial plants mitigation strategy multidimensional data visualization parallel coordinates plot process control process control hazard identification process plant production engineering computing software package software packages

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A New Axes Re-ordering Method in Parallel Coordinates visualization

A New Axes Re-ordering Method in Parallel Coordinates Visual...

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11th IEEE International Conference on Machine Learning and Applications (ICMLA)

作者： Lu, Liang Fu Huang, Mao Lin Huang, Tze-Haw Univ Technol Fac Engn & Informat Technol Sch Software Sydney NSW Australia

ISBN: (纸本)9780769549132

visualization and interaction of multidimensional data always requires optimized solutions to integrate the display, exploration and analytical reasoning of data into one visual pipeline for human-centered data analysis and interpretation. Parallel coordinate, as one of the popular multidimensional data visualization techniques, is suffered from the visual clutter problem. Though changing the ordering of axis is a straightforward way to address it, optimizing the order of axis is a NP-complete problem. In this paper, we propose a new axes re-ordering method in parallel coordinates visualization: a similarity-based method, which is based on the combination of Nonlinear Correlation Coefficient (NCC) and Singular Value Decomposition (SVD) algorithms. By using this approach, the first remarkable axe can be selected based on mathematics theory and all axis are re-ordered in line with the degree of similarities among them. Meanwhile, we would also propose a measurement of contribution rate of each dimension to reveal the property hidden in the dataset. At last, case studies demonstrate the rationale and effectiveness of our approaches: NCC reordering method can enlarge the mean crossing angles and reduce the amount of polylines between the neighboring axes. It can reduce the computational complexity greatly in comparison with other re-ordering methods.

关键词： multidimensional data visualization visual analytics parallel coordinates axes re-ordering singular value decomposition nonlinear correlation coefficient

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Spiro: Order-Preserving visualization in High Performance Computing Monitoring 18th

Spiro: Order-Preserving Visualization in High Performance Co...

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18th International Symposium on Visual Computing (ISVC)

作者： Dang, Tommy Nguyen, Ngan V. T. Li, Jie Sill, Alan Chen, Yong Texas Tech Univ Lubbock TX 79409 USA

ISBN: (纸本)9783031479687;9783031479694

As High-Performance Computing (HPC) is moving closer to entering the exascale era, monitoring HPC systems is becoming increasingly important and continuously more complex. The innovations in interactive visualization provide tremendous assistance in presenting complex HPC data to be more comprehensible and promote the roles of humans in the analysis process. With the larger number of system components in the monitoring tasks, representing them in order requires a scalable mechanism for handling multiple visual elements and simultaneously preserving the context of ordering. In this paper, we propose a novel method for utilizing the spiral layout for order-preserving visualization in HPC monitoring, called Spiro. To demonstrate the effectiveness and usefulness of Spiro, we present the case studies of the application to a real-world temporal, multivariate HPC dataset.

关键词： High-Performance Computing Center RESTful API Time-Series data Analysis Spiral Layout Multivariate Clustering multidimensional data visualization

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Visual Analytics of multidimensional Oral Health Surveys: data Mining Study

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JMIR MEDICAL INFORMATICS 2023年 11卷 e46275页

作者： Xu, Ting Ma, Yuming Pan, Tianya Chen, Yifei Liu, Yuhua Zhu, Fudong Zhou, Zhiguang Chen, Qianming Zhejiang Univ Affiliated Hosp 1 Dept Stomatol Hangzhou Peoples R China Hangzhou Dianzi Univ Sch Media & Design Xueyuan Rd 18 Hangzhou 310018 Peoples R China Zhejiang Univ Affiliated Stomatol Hosp Hangzhou Peoples R China

Background: Oral health surveys largely facilitate the prevention and treatment of oral diseases as well as the awareness of population health status. As oral health is always surveyed from a variety of perspectives, it is a difficult and complicated task to gain insights from multidimensional oral health ***: We aimed to develop a visualization framework for the visual analytics and deep mining of multidimensional oral health ***: First, diseases and groups were embedded into data portraits based on their multidimensional attributes. Subsequently, group classification and correlation pattern extraction were conducted to explore the correlation features among diseases, behaviors, symptoms, and cognitions. On the basis of the feature mining of diseases, groups, behaviors, and their attributes, a knowledge graph was constructed to reveal semantic information, integrate the graph query function, and describe the features of intrigue to users. Results: A visualization framework was implemented for the exploration of multidimensional oral health surveys. A series of user-friendly interactions were integrated to propose a visual analysis system that can help users further achieve the regulations of oral health ***: A visualization framework is provided in this paper with a set of meaningful user interactions integrated, enabling users to intuitively understand the oral health situation and conduct in-depth data exploration and analysis. Case studies based on real-world data sets demonstrate the effectiveness of our system in the exploration of oral diseases.

关键词： visual analytics oral health data mining knowledge graph multidimensional data visualization

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Hierarchical pixel bar charts

Hierarchical pixel bar charts

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7th IEEE Symposium on Information visualization (INFOVIS 2001)

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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Visualizing differentially expressed genes

Visualizing differentially expressed genes

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Conference on Photonic Devices and Algorithms for Computing VIII

作者： Islam, Atiq U. Iftekharuddin, Khan M. Russomanno, David J. Univ Memphis Dept Elect & Comp Engn Memphis TN 38152 USA

ISBN: (纸本)0819463892

Identification of significantly differentially expressed genes (marker genes) among sample groups is a central issue in microarray analysis. This identification is important to understand the molecular pathway of diseases. Many statistical methods have been proposed to locate marker genes. These methods depend on a cutoff value for selection. A fight-fisted cutoff may omit some of the important marker genes, whereas a generous threshold increases the number of false positives. Although robust models for identifying marker genes more accurately is an area of intense research, effective tools for the evaluation of results is often ignored in the literature. Despite the robustness of many of these methods, there is always some probability of false positives. In this paper, we propose a novel approach that exploits parallel coordinates to visualize the gene expression patterns so that one can compare the expression level changes of the marker genes between sample groups and determine whether the selected marker genes are valid. Such visualization is useful to measure the validity of the marker gene selection process as well as to fine tune the parameters of a particular method. A prediction method based on the selected marker genes is used to measure the reliability of our process.

关键词： Central Nervous System (CNS) tumor differentially expressed gene DNA microarray statistical analysis multidimensional data visualization parallel coordinates

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Visual Interfaces for Recommendation Systems: Finding Similar and Dissimilar Peers

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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2019年第1期10卷 9-9页

作者： Du, Fan Plaisant, Catherine Spring, Neil Shneiderman, Ben Univ Maryland Dept Comp Sci AV Williams Bldg8223 Pain Branch Dr College Pk MD 20742 USA

Recommendation applications can guide users in making important life choices by referring to the activities of similar peers. For example, students making academic plans may learn from the data of similar students, while patients and their physicians may explore data from similar patients to select the best treatment. Selecting an appropriate peer group has a strong impact on the value of the guidance that can result from analyzing the peer group data. In this article, we describe a visual interface that helps users review the similarity and differences between a seed record and a group of similar records and refine the selection. We introduce the LikeMeDonuts, Ranking Glyph, and History Heatmap visualizations. The interface was refined through three rounds of formative usability evaluation with 12 target users, and its usefulness was evaluated by a case study with a student review manager using real student data. We describe three analytic workflows observed during use and summarize how users' input shaped the final design.

关键词： Similarity personal record multidimensional data visualization temporal visualization decision making visual analytics

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EventAction: A Visual Analytics Approach to Explainable Recommendation for Event Sequences

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ACM TRANSACTIONS ON INTERACTIVE INTELLIGENT SYSTEMS 2019年第4期9卷 21-21页

作者： Du, Fan Plaisant, Catherine Spring, Neil Crowley, Kenyon Shneiderman, Ben Univ Maryland 8125 Paint Branch Dr College Pk MD 20742 USA Univ Maryland 2117 Hornbake BldgSouth Wing College Pk MD 20742 USA Univ Maryland 7699 Mowatt Ln College Pk MD 20742 USA

People use recommender systems to improve their decisions;for example, item recommender systems help them find films to watch or books to buy. Despite the ubiquity of item recommender systems, they can be improved by giving users greater transparency and control. This article develops and assesses interactive strategies for transparency and control, as applied to event sequence recommender systems, which provide guidance in critical life choices such as medical treatments, careers decisions, and educational course selections. This article's main contribution is the use of both record attributes and temporal event information as features to identify similar records and provide appropriate recommendations. While traditional item recommendations are based on choices by people with similar attributes, such as those who looked at this product or watched this movie, our event sequence recommendation approach allows users to select records that share similar attribute values and start with a similar event sequence. Then users see how different choices of actions and the orders and times between them might lead to users' desired outcomes. This paper applies a visual analytics approach to present and explain recommendations of event sequences. It presents a workflow for event sequence recommendation that is implemented in EventAction and reports on three case studies in two domains to illustrate the use of generating event sequence recommendations based on personal histories. It also offers design guidelines for the construction of user interfaces for event sequence recommendation and discusses ethical issues in dealing with personal histories.

关键词： Similarity personal record multidimensional data visualization temporal visualization decision making visual analytics

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Visual Steering Commands for Trade Space Exploration: User-Guided Sampling With Example

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2009年第4期9卷 44501-44501页

作者： Stump, Gary Lego, Sara Yukish, Mike Simpson, Timothy W. Donndelinger, Joseph A. Penn State Univ Dept Mech & Nucl Engn University Pk PA 16802 USA Penn State Univ Appl Res Lab State Coll PA 16804 USA Gen Motors Res & Dev Ctr Vehicle Dev Res Lab Warren MI 48090 USA

Recent advancements in computing power and speed provide opportunities to revolutionize trade space exploration, particularly for the design of complex systems such as automobiles, aircraft, and spacecraft. In this paper, we introduce three visual steering commands to support trade space exploration and demonstrate their use within a powerful data visualization tool that allows designers to explore multidimensional trade spaces using glyph, 1D and 2D histograms, 2D scatter, scatter matrix, and parallel coordinate plots, linked views, brushing, preference shading, and Pareto frontier display. In particular, we define three user-guided samplers that enable designers to explore (1) the entire design space, (2) near a point of interest, or (3) within a region of high preference. We illustrate these three samplers with a vehicle configuration model that evaluates the technical feasibility of new vehicle concepts. Future research is also discussed. [DOI: 10.1115/1.3243633]

关键词： multidimensional data visualization trade space exploration visual steering

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