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2019 IEEE Visualization Conference (VIS)最新文献

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ElectroLens: Understanding Atomistic Simulations through Spatially-Resolved Visualization of High-Dimensional Features 电子透镜:通过高维特征的空间分辨可视化来理解原子模拟
2019 IEEE Visualization Conference (VIS) Pub Date : 2019-08-20 DOI: 10.1109/VISUAL.2019.8933647
Xiangyun Lei, Fred Hohman, Duen Horng Chau, A. Medford
{"title":"ElectroLens: Understanding Atomistic Simulations through Spatially-Resolved Visualization of High-Dimensional Features","authors":"Xiangyun Lei, Fred Hohman, Duen Horng Chau, A. Medford","doi":"10.1109/VISUAL.2019.8933647","DOIUrl":"https://doi.org/10.1109/VISUAL.2019.8933647","url":null,"abstract":"In recent years, machine learning (ML) has gained significant popularity in the field of chemical informatics and electronic structure theory. These techniques often require researchers to engineer abstract \"features\" that encode chemical concepts into a mathematical form compatible with the input to machine-learning models. However, there is no existing tool to connect these abstract features back to the actual chemical system, making it difficult to diagnose failures and to build intuition about the meaning of the features. We present ElectroLens, a new visualization tool for high-dimensional spatially-resolved features to tackle this problem. The tool visualizes high-dimensional data sets for atomistic and electron environment features by a series of linked 3D views and 2D plots. The tool is able to connect different derived features and their corresponding regions in 3D via interactive selection. It is built to be scalable, and integrate with existing infrastructure.","PeriodicalId":192801,"journal":{"name":"2019 IEEE Visualization Conference (VIS)","volume":"47 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114024384","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: 2
Thumbnails for Data Stories: A Survey of Current Practices 数据故事的缩略图:当前实践的调查
2019 IEEE Visualization Conference (VIS) Pub Date : 2019-08-19 DOI: 10.1109/VISUAL.2019.8933773
Hwiyeon Kim, J. Oh, Yunha Han, Sungahn Ko, M. Brehmer, B. Kwon
{"title":"Thumbnails for Data Stories: A Survey of Current Practices","authors":"Hwiyeon Kim, J. Oh, Yunha Han, Sungahn Ko, M. Brehmer, B. Kwon","doi":"10.1109/VISUAL.2019.8933773","DOIUrl":"https://doi.org/10.1109/VISUAL.2019.8933773","url":null,"abstract":"When people browse online news, small thumbnail images accompanying links to articles attract their attention and help them to decide which articles to read. As an increasing proportion of online news can be construed as data journalism, we have witnessed a corresponding increase in the incorporation of visualization in article thumbnails. However, there is little research to support alternative design choices for visualization thumbnails, which include resizing, cropping, simplifying, and embellishing charts appearing within the body of the associated article. We therefore sought to better understand these design choices and determine what makes a visualization thumbnail inviting and interpretable. This paper presents our findings from a survey of visualization thumbnails collected online and from conversations with data journalists and news graphics designers. Our study reveals that there exists an uncharted design space, one that is in need of further empirical study. Our work can thus be seen as a first step toward providing structured guidance on how to design thumbnails for data stories.","PeriodicalId":192801,"journal":{"name":"2019 IEEE Visualization Conference (VIS)","volume":"354 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115467261","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: 8
SAX Navigator: Time Series Exploration through Hierarchical Clustering SAX导航器:通过层次聚类的时间序列探索
2019 IEEE Visualization Conference (VIS) Pub Date : 2019-08-15 DOI: 10.1109/VISUAL.2019.8933618
N. Ruta, N. Sawada, K. McKeough, M. Behrisch, J. Beyer
{"title":"SAX Navigator: Time Series Exploration through Hierarchical Clustering","authors":"N. Ruta, N. Sawada, K. McKeough, M. Behrisch, J. Beyer","doi":"10.1109/VISUAL.2019.8933618","DOIUrl":"https://doi.org/10.1109/VISUAL.2019.8933618","url":null,"abstract":"Comparing many long time series is challenging to do by hand. Clustering time series enables data analysts to discover relevance between and anomalies among multiple time series. However, even after reasonable clustering, analysts have to scrutinize correlations between clusters or similarities within a cluster. We developed SAX Navigator, an interactive visualization tool, that allows users to hierarchically explore global patterns as well as individual observations across large collections of time series data. Our visualization provides a unique way to navigate time series that involves a \"vocabulary of patterns\" developed by using a dimensionality reduction technique, Symbolic Aggregate approXimation (SAX). With SAX, the time series data clusters efficiently and is quicker to query at scale. We demonstrate the ability of SAX Navigator to analyze patterns in large time series data based on three case studies for an astronomy data set. We verify the usability of our system through a think-aloud study with an astronomy domain scientist.","PeriodicalId":192801,"journal":{"name":"2019 IEEE Visualization Conference (VIS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132062003","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: 11
Visualization Assessment: A Machine Learning Approach 可视化评估:一种机器学习方法
2019 IEEE Visualization Conference (VIS) Pub Date : 2019-08-07 DOI: 10.1109/VISUAL.2019.8933570
Xin Fu, Yun Wang, Haoyu Dong, Weiwei Cui, Haidong Zhang
{"title":"Visualization Assessment: A Machine Learning Approach","authors":"Xin Fu, Yun Wang, Haoyu Dong, Weiwei Cui, Haidong Zhang","doi":"10.1109/VISUAL.2019.8933570","DOIUrl":"https://doi.org/10.1109/VISUAL.2019.8933570","url":null,"abstract":"Researchers assess visualizations from multiple aspects, such as aesthetics, memorability, engagement, and efficiency. However, these assessments are mostly carried out through user studies. There is a lack of automatic visualization assessment approaches, which hinders further applications like visualization recommendation, indexing, and generation. In this paper, we propose automating the visualization assessment process with modern machine learning approaches. We utilize a semi-supervised learning method, which first employs Variational Autoencoder (VAE) to learn effective features from visualizations, subsequently training machine learning models for different assessment tasks. Then, we can automatically assess new visualization images by predicting their scores or rankings with the trained model. To evaluate our method, we run two different assessment tasks, namely, aesthetics and memorability, on different visualization datasets. Experiments show that our method can learn effective visual features and achieves good performance on these assessment tasks.","PeriodicalId":192801,"journal":{"name":"2019 IEEE Visualization Conference (VIS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126108314","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: 21
Sabrina: Modeling and Visualization of Financial Data over Time with Incremental Domain Knowledge 萨布丽娜:随着时间的推移，金融数据的建模和可视化与增量领域知识
2019 IEEE Visualization Conference (VIS) Pub Date : 2019-08-05 DOI: 10.1109/VISUAL.2019.8933598
Alessio Arleo, J. Sorger, Christos Tsigkanos, Chao Jia, R. Leite, Ilir Murturi, Manfred Klaffenböck, S. Dustdar, M. Wimmer, S. Miksch
{"title":"Sabrina: Modeling and Visualization of Financial Data over Time with Incremental Domain Knowledge","authors":"Alessio Arleo, J. Sorger, Christos Tsigkanos, Chao Jia, R. Leite, Ilir Murturi, Manfred Klaffenböck, S. Dustdar, M. Wimmer, S. Miksch","doi":"10.1109/VISUAL.2019.8933598","DOIUrl":"https://doi.org/10.1109/VISUAL.2019.8933598","url":null,"abstract":"Investment planning requires knowledge of the financial landscape on a large scale, both in terms of geo-spatial and industry sector distribution. There is plenty of data available, but it is scattered across heterogeneous sources (newspapers, open data, etc.), which makes it difficult for financial analysts to understand the big picture. In this paper, we present Sabrina, a financial data analysis and visualization approach that incorporates a pipeline for the generation of firm-to-firm financial transaction networks. The pipeline is capable of fusing the ground truth on individual firms in a region with (incremental) domain knowledge on general macroscopic aspects of the economy. Sabrina unites these heterogeneous data sources within a uniform visual interface that enables the visual analysis process. In a user study with three domain experts, we illustrate the usefulness of Sabrina, which eases their analysis process.","PeriodicalId":192801,"journal":{"name":"2019 IEEE Visualization Conference (VIS)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121861751","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: 7
Efficient Space Skipping and Adaptive Sampling of Unstructured Volumes Using Hardware Accelerated Ray Tracing 基于硬件加速光线追踪的非结构化体的有效空间跳跃和自适应采样
2019 IEEE Visualization Conference (VIS) Pub Date : 2019-08-05 DOI: 10.1109/VISUAL.2019.8933539
N. Morrical, W. Usher, I. Wald, Valerio Pascucci
{"title":"Efficient Space Skipping and Adaptive Sampling of Unstructured Volumes Using Hardware Accelerated Ray Tracing","authors":"N. Morrical, W. Usher, I. Wald, Valerio Pascucci","doi":"10.1109/VISUAL.2019.8933539","DOIUrl":"https://doi.org/10.1109/VISUAL.2019.8933539","url":null,"abstract":"Sample based ray marching is an effective method for direct volume rendering of unstructured meshes. However, sampling such meshes remains expensive, and strategies to reduce the number of samples taken have received relatively little attention. In this paper, we introduce a method for rendering unstructured meshes using a combination of a coarse spatial acceleration structure and hardware-accelerated ray tracing. Our approach enables efficient empty space skipping and adaptive sampling of unstructured meshes, and outperforms a reference ray marcher by up to 7×.","PeriodicalId":192801,"journal":{"name":"2019 IEEE Visualization Conference (VIS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114758880","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: 33
Interactive Visualisation of Hierarchical Quantitative Data: An Evaluation 层次定量数据的交互式可视化:评价
2019 IEEE Visualization Conference (VIS) Pub Date : 2019-08-04 DOI: 10.1109/VISUAL.2019.8933545
Linda Woodburn, Yalong Yang, K. Marriott
{"title":"Interactive Visualisation of Hierarchical Quantitative Data: An Evaluation","authors":"Linda Woodburn, Yalong Yang, K. Marriott","doi":"10.1109/VISUAL.2019.8933545","DOIUrl":"https://doi.org/10.1109/VISUAL.2019.8933545","url":null,"abstract":"We have compared three common visualisations for hierarchical quantitative data, treemaps, icicle plots and sunburst charts as well as a semicircular variant of sunburst charts we call the sundown chart. In a pilot study, we found that the sunburst chart was least preferred. In a controlled study with 12 participants, we compared treemaps, icicle plots and sundown charts. Treemap was the least preferred and had a slower performance on a basic navigation task and slower performance and accuracy in hierarchy understanding tasks. The icicle plot and sundown chart had similar performance with slight user preference for the icicle plot.","PeriodicalId":192801,"journal":{"name":"2019 IEEE Visualization Conference (VIS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117341285","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: 18
FeatureExplorer: Interactive Feature Selection and Exploration of Regression Models for Hyperspectral Images Feature explorer:高光谱图像的交互式特征选择和回归模型探索
2019 IEEE Visualization Conference (VIS) Pub Date : 2019-08-02 DOI: 10.1109/VISUAL.2019.8933619
Jieqiong Zhao, M. Karimzadeh, A. Masjedi, Taojun Wang, Xiwen Zhang, M. Crawford, D. Ebert
{"title":"FeatureExplorer: Interactive Feature Selection and Exploration of Regression Models for Hyperspectral Images","authors":"Jieqiong Zhao, M. Karimzadeh, A. Masjedi, Taojun Wang, Xiwen Zhang, M. Crawford, D. Ebert","doi":"10.1109/VISUAL.2019.8933619","DOIUrl":"https://doi.org/10.1109/VISUAL.2019.8933619","url":null,"abstract":"Feature selection is used in machine learning to improve predictions, decrease computation time, reduce noise, and tune models based on limited sample data. In this article, we present FeatureExplorer, a visual analytics system that supports the dynamic evaluation of regression models and importance of feature subsets through the interactive selection of features in high-dimensional feature spaces typical of hyperspectral images. The interactive system allows users to iteratively refine and diagnose the model by selecting features based on their domain knowledge, interchangeable (correlated) features, feature importance, and the resulting model performance.","PeriodicalId":192801,"journal":{"name":"2019 IEEE Visualization Conference (VIS)","volume":"114 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120886359","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: 19
Analyzing Time Attributes in Temporal Event Sequences 分析时间事件序列中的时间属性
2019 IEEE Visualization Conference (VIS) Pub Date : 2019-08-02 DOI: 10.1109/VISUAL.2019.8933770
J. Magallanes, Lindsey van Gemeren, Steven Wood, M. Villa-Uriol
{"title":"Analyzing Time Attributes in Temporal Event Sequences","authors":"J. Magallanes, Lindsey van Gemeren, Steven Wood, M. Villa-Uriol","doi":"10.1109/VISUAL.2019.8933770","DOIUrl":"https://doi.org/10.1109/VISUAL.2019.8933770","url":null,"abstract":"Event data is present in a variety of domains such as electronic health records, daily living activities and web clickstream records. Current visualization methods to explore event data focus on discovering sequential patterns but present limitations when studying time attributes in event sequences. Time attributes are especially important when studying waiting times or lengths of visit in patient flow analysis. We propose a visual analytics methodology that allows the identification of trends and outliers in respect of duration and time of occurrence in event sequences. The proposed method presents event data using a single Sequential and Time Patterns overview. User-driven alignment by multiple events, sorting by sequence similarity and a novel visual encoding of events allows the comparison of time trends across and within sequences. The proposed visualization allows the derivation of findings that otherwise could not be obtained using traditional visualizations. The proposed methodology has been applied to a real-world dataset provided by Sheffield Teaching Hospitals NHS Foundation Trust, for which four classes of conclusions were derived.","PeriodicalId":192801,"journal":{"name":"2019 IEEE Visualization Conference (VIS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125243941","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: 2
Visualizing RNN States with Predictive Semantic Encodings 用预测语义编码可视化RNN状态
2019 IEEE Visualization Conference (VIS) Pub Date : 2019-08-01 DOI: 10.1109/VISUAL.2019.8933744
Lindsey Sawatzky, S. Bergner, F. Popowich
{"title":"Visualizing RNN States with Predictive Semantic Encodings","authors":"Lindsey Sawatzky, S. Bergner, F. Popowich","doi":"10.1109/VISUAL.2019.8933744","DOIUrl":"https://doi.org/10.1109/VISUAL.2019.8933744","url":null,"abstract":"Recurrent Neural Networks are an effective and prevalent tool used to model sequential data such as natural language text. However, their deep nature and massive number of parameters pose a challenge for those intending to study precisely how they work. We present a visual technique that gives a high level intuition behind the semantics of the hidden states within Recurrent Neural Networks. This semantic encoding allows for hidden states to be compared throughout the model independent of their internal details. The proposed technique is displayed in a proof of concept visualization tool which is demonstrated to visualize the natural language processing task of language modelling.","PeriodicalId":192801,"journal":{"name":"2019 IEEE Visualization Conference (VIS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127613173","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: 5
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