2020 IEEE Pacific Visualization Symposium (PacificVis)最新文献_第2页

PacificVis 2020 Contents PacificVis 2020目录

2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-06-01 DOI: 10.1109/pacificvis48177.2020.9086197

引用次数: 0

Representing Multivariate Data by Optimal Colors to Uncover Events of Interest in Time Series Data 用最优颜色表示多元数据以揭示时间序列数据中感兴趣的事件

2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-06-01 DOI: 10.1109/PacificVis48177.2020.9915

Ding-Bang Chen, Chien-Hsun Lai, Yun-Hsuan Lien, Yu-Hsuan Lin, Yu-Shuen Wang, K. Ma

{"title":"Representing Multivariate Data by Optimal Colors to Uncover Events of Interest in Time Series Data","authors":"Ding-Bang Chen, Chien-Hsun Lai, Yun-Hsuan Lien, Yu-Hsuan Lin, Yu-Shuen Wang, K. Ma","doi":"10.1109/PacificVis48177.2020.9915","DOIUrl":"https://doi.org/10.1109/PacificVis48177.2020.9915","url":null,"abstract":"In this paper, we present a visualization system for users to study multivariate time series data. They first identify trends or anomalies from a global view and then examine details in a local view. Specifically, we train a neural network to project high-dimensional data to a two dimensional (2D) planar space while retaining global data distances. By aligning the 2D points with a predefined color map, high-dimensional data can be represented by colors. Because perceptual color differentiation may fail to reflect data distance, we optimize perceptual color differentiation on each map region by deformation. The region with large perceptual color differentiation will expand, whereas the region with small differentiation will shrink. Since colors do not occupy any space in visualization, we convey the overview of multivariate time series data by a calendar view. Cells in the view are color-coded to represent multivariate data at different time spans. Users can observe color changes over time to identify events of interest. Afterward, they study details of an event by examining parallel coordinate plots. Cells in the calendar view and the parallel coordinate plots are dynamically linked for users to obtain insights that are barely noticeable in large datasets. The experiment results, comparisons, conducted case studies, and the user study indicate that our visualization system is feasible and effective.","PeriodicalId":322092,"journal":{"name":"2020 IEEE Pacific Visualization Symposium (PacificVis)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132470024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

PacificVis 2020 Chair Message 太平洋2020年主席致辞

2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-06-01 DOI: 10.1109/pacificvis48177.2020.9086225

引用次数: 0

Space-Reclaiming Icicle Plots 空间回收冰柱图

2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-06-01 DOI: 10.1109/PacificVis48177.2020.4908

H. V. D. Wetering, Nico Klaassen, Michael Burch

引用次数: 11

2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-06-01 DOI: 10.1109/pacificvis48177.2020.9086188

引用次数: 0

Towards Rigorously Designed Preference Visualizations for Group Decision Making 面向群体决策的严格设计偏好可视化

2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-06-01 DOI: 10.1109/PacificVis48177.2020.5111

Emily Hindalong, Jordon Johnson, G. Carenini, T. Munzner

引用次数: 4

Efficient Morphing of Shape-preserving Star Coordinates 保形星坐标的有效变形

2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-06-01 DOI: 10.1109/PacificVis48177.2020.8199

V. Molchanov, Sagad Hamid, L. Linsen

{"title":"Efficient Morphing of Shape-preserving Star Coordinates","authors":"V. Molchanov, Sagad Hamid, L. Linsen","doi":"10.1109/PacificVis48177.2020.8199","DOIUrl":"https://doi.org/10.1109/PacificVis48177.2020.8199","url":null,"abstract":"Data tours follow an exploratory multi-dimensional data visualization concept that provides animations of projections of the multidimensional data to a 2D visual space. To create an animation, a sequence of key projections is provided and morphings between each pair of consecutive key projections are computed, which then can be stitched together to form the data tour. The morphings should be smooth so that a user can easily follow the transformations, and their computations shall be fast to allow for their integration into an interactive visual exploration process. Moreover, if the key projections are chosen to satisfy additional conditions, it is desirable that these conditions are maintained during morphing. Shape preservation is such a desirable condition, as it avoids shape distortions that may otherwise be caused by a projection. We develop a novel efficient morphing algorithms for computing shape-preserving data tours, i.e., data tours constructed for a sequence of shape-preserving linear projections. We propose a stepping strategy for the morphing to avoid discontinuities in the evolution of the projections, where we represent the linear projections using a star-coordinates system. Our algorithms are less computationally involved, produce smoother morphings, and require less user-defined parameter settings than existing state-of-the-art approaches.","PeriodicalId":322092,"journal":{"name":"2020 IEEE Pacific Visualization Symposium (PacificVis)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123813769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Tensor Spines - A Hyperstreamlines Variant Suitable for Indefinite Symmetric Second-Order Tensors 张量棘——一种适用于不定对称二阶张量的超流线变体

2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-06-01 DOI: 10.1109/PacificVis48177.2020.1008

Vanessa Kretzschmar, Fabian Günther, M. Stommel, G. Scheuermann

引用次数: 5

Quality Metrics for Symmetric Graph Drawings * 对称图形绘图的质量指标*

2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-06-01 DOI: 10.1109/PacificVis48177.2020.1022

A. Meidiana, Seok-Hee Hong, P. Eades, D. Keim

引用次数: 6

SSR-VFD: Spatial Super-Resolution for Vector Field Data Analysis and Visualization SSR-VFD:空间超分辨率矢量场数据分析和可视化

2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-06-01 DOI: 10.1109/PacificVis48177.2020.8737

Li Guo, Shaojie Ye, Jun Han, Hao Zheng, Han Gao, D. Chen, Jian-Xun Wang, Chaoli Wang

{"title":"SSR-VFD: Spatial Super-Resolution for Vector Field Data Analysis and Visualization","authors":"Li Guo, Shaojie Ye, Jun Han, Hao Zheng, Han Gao, D. Chen, Jian-Xun Wang, Chaoli Wang","doi":"10.1109/PacificVis48177.2020.8737","DOIUrl":"https://doi.org/10.1109/PacificVis48177.2020.8737","url":null,"abstract":"We present SSR-VFD, a novel deep learning framework that produces coherent spatial super-resolution (SSR) of three-dimensional vector field data (VFD). SSR-VFD is the first work that advocates a machine learning approach to generate high-resolution vector fields from low-resolution ones. The core of SSR-VFD lies in the use of three separate neural nets that take the three components of a low-resolution vector field as input and jointly output a synthesized high-resolution vector field. To capture spatial coherence, we take into account magnitude and angle losses in network optimization. Our method can work in the in situ scenario where VFD are down-sampled at simulation time for storage saving and these reduced VFD are upsampled back to their original resolution during postprocessing. To demonstrate the effectiveness of SSR-VFD, we show quantitative and qualitative results with several vector field data sets of different characteristics and compare our method against volume upscaling using bicubic interpolation, and two solutions based on CNN and GAN, respectively.","PeriodicalId":322092,"journal":{"name":"2020 IEEE Pacific Visualization Symposium (PacificVis)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128340602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 40