2007 IEEE Symposium on Visual Analytics Science and Technology最新文献_第3页

Point Placement by Phylogenetic Trees and its Application to Visual Analysis of Document Collections 系统发育树的点定位及其在文献馆藏可视化分析中的应用

2007 IEEE Symposium on Visual Analytics Science and Technology Pub Date : 2007-10-30 DOI: 10.1109/VAST.2007.4389002

A. M. Cuadros, F. Paulovich, R. Minghim, G. P. Telles

引用次数: 51

Formalizing Analytical Discourse in Visual Analytics 形式化视觉分析中的分析话语

2007 IEEE Symposium on Visual Analytics Science and Technology Pub Date : 2007-10-30 DOI: 10.1109/VAST.2007.4389025

Guoray Cai

引用次数: 4

From Tasks to Tools: A Field Study in Collaborative Visual Analytics 从任务到工具:协同视觉分析的实地研究

2007 IEEE Symposium on Visual Analytics Science and Technology Pub Date : 2007-10-30 DOI: 10.1109/VAST.2007.4389028

Daniel Ha, Minjung Kim, Andrew Wade, William-S Chao, Kevin I.-J. Ho, Linda T. Kaastra, Brian D. Fisher, J. Dill

引用次数: 1

Literature Fingerprinting: A New Method for Visual Literary Analysis 文学指纹:视觉文学分析的新方法

2007 IEEE Symposium on Visual Analytics Science and Technology Pub Date : 2007-10-30 DOI: 10.1109/VAST.2007.4389004

D. Keim, Daniela Oelke

引用次数: 128

FemaRepViz: Automatic Extraction and Geo-Temporal Visualization of FEMA National Situation Updates FemaRepViz: FEMA国家情况更新的自动提取和地理-时间可视化

2007 IEEE Symposium on Visual Analytics Science and Technology Pub Date : 2007-10-30 DOI: 10.1109/VAST.2007.4388991

C. Pan, P. Mitra

{"title":"FemaRepViz: Automatic Extraction and Geo-Temporal Visualization of FEMA National Situation Updates","authors":"C. Pan, P. Mitra","doi":"10.1109/VAST.2007.4388991","DOIUrl":"https://doi.org/10.1109/VAST.2007.4388991","url":null,"abstract":"An architecture for visualizing information extracted from text documents is proposed. In conformance with this architecture, a toolkit, FemaRepViz, has been implemented to extract and visualize temporal, geospatial, and summarized information from FEMA national update reports. Preliminary tests have shown satisfactory accuracy for FEMARepViz. A central component of the architecture is an entity extractor that extracts named entities like person names, location names, temporal references, etc. FEMARepViz is based on FactXtractor, an entity-extractor that works on text documents. The information extracted using FactXtractor is processed using GeoTagger, a geographical name disambiguation tool based on a novel clustering-based disambiguation algorithm. To extract relationships among entities, we propose a machine-learning based algorithm that uses a novel stripped dependency tree kernel. We illustrate and evaluate the usefulness of our system on the FEMA National Situation Updates. Daily reports are fetched by FEMARepViz from the FEMA website, segmented into coherent sections and each section is classified into one of several known incident types. We use concept Vista, Google maps and Google earth to visualize the events extracted from the text reports and allow the user to interactively filter the topics, locations, and time-periods of interest to create a visual analytics toolkit that is useful for rapid analysis of events reported in a large set of text documents.","PeriodicalId":227910,"journal":{"name":"2007 IEEE Symposium on Visual Analytics Science and Technology","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115096267","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

Outlook for Visual Analytics Research Funding 视觉分析研究经费展望

2007 IEEE Symposium on Visual Analytics Science and Technology Pub Date : 2007-10-30 DOI: 10.1109/VAST.2007.4389030

James J. Thomas, D. Keim, Joseph Kielman, Larry Rosenblum

{"title":"Outlook for Visual Analytics Research Funding","authors":"James J. Thomas, D. Keim, Joseph Kielman, Larry Rosenblum","doi":"10.1109/VAST.2007.4389030","DOIUrl":"https://doi.org/10.1109/VAST.2007.4389030","url":null,"abstract":"Visual Analytics has become a rapidly growing field of study. It is also a field that is addressing very significant real world problems in homeland security, business analytics, emergency management, genetics and bioinformatics, investigative analysis, medical analytics, and other areas. For both these reasons, it is attracting new funding and will continue to do so in the future. Visual analytics has also become an international field, with significant research efforts in Canada, Europe, and Australia, as well as the U.S. There is significant new research funding in Canada and Germany with other efforts being discussed, including a major program sponsored by the European Union. The contributors to this panel are some of the primary thought leaders providing research funding or involved in setting up the funding apparatus. We have asked them to present their needs, funding programs, and expectations from the research community. They all come from different perspectives, different missions, and different expectations. They will present their views of the range of activity in both the U.S. and internationally and discuss what is coming. Come learn about these programs, initiatives, and plans, and how you can contribute.","PeriodicalId":227910,"journal":{"name":"2007 IEEE Symposium on Visual Analytics Science and Technology","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129753904","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: 0

Analysis Guided Visual Exploration of Multivariate Data 分析引导多元数据的可视化探索

2007 IEEE Symposium on Visual Analytics Science and Technology Pub Date : 2007-10-30 DOI: 10.1109/VAST.2007.4389000

Di Yang, Elke A. Rundensteiner, M. Ward

{"title":"Analysis Guided Visual Exploration of Multivariate Data","authors":"Di Yang, Elke A. Rundensteiner, M. Ward","doi":"10.1109/VAST.2007.4389000","DOIUrl":"https://doi.org/10.1109/VAST.2007.4389000","url":null,"abstract":"Visualization systems traditionally focus on graphical representation of information. They tend not to provide integrated analytical services that could aid users in tackling complex knowledge discovery tasks. Users' exploration in such environments is usually impeded due to several problems: 1) valuable information is hard to discover when too much data is visualized on the screen; 2) Users have to manage and organize their discoveries off line, because no systematic discovery management mechanism exists; 3) their discoveries based on visual exploration alone may lack accuracy; 4) and they have no convenient access to the important knowledge learned by other users. To tackle these problems, it has been recognized that analytical tools must be introduced into visualization systems. In this paper, we present a novel analysis-guided exploration system, called the nugget management system (NMS). It leverages the collaborative effort of human comprehensibility and machine computations to facilitate users' visual exploration processes. Specifically, NMS first extracts the valuable information (nuggets) hidden in datasets based on the interests of users. Given that similar nuggets may be re-discovered by different users, NMS consolidates the nugget candidate set by clustering based on their semantic similarity. To solve the problem of inaccurate discoveries, localized data mining techniques are applied to refine the nuggets to best represent the captured patterns in datasets. Lastly, the resulting well-organized nugget pool is used to guide users' exploration. To evaluate the effectiveness of NMS, we integrated NMS into Xmd- vTool, a freeware multivariate visualization system. User studies were performed to compare the users' efficiency and accuracy in finishing tasks on real datasets, with and without the help of NMS. Our user studies confirmed the effectiveness of NMS.","PeriodicalId":227910,"journal":{"name":"2007 IEEE Symposium on Visual Analytics Science and Technology","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129766824","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: 52

VAST 2007 Contest TexPlorer 浩瀚2007竞赛TexPlorer

2007 IEEE Symposium on Visual Analytics Science and Technology Pub Date : 2007-10-30 DOI: 10.1109/VAST.2007.4389037

C. Pan, Anuj R. Jaiswal, Junyan Luo, A. Robinson, P. Mitra, A. MacEachren, I. Turton

引用次数: 1

ClusterSculptor: A Visual Analytics Tool for High-Dimensional Data ClusterSculptor:用于高维数据的可视化分析工具

2007 IEEE Symposium on Visual Analytics Science and Technology Pub Date : 2007-10-30 DOI: 10.1109/VAST.2007.4388999

E. J. Nam, Yiping Han, K. Mueller, A. Zelenyuk, D. Imre

{"title":"ClusterSculptor: A Visual Analytics Tool for High-Dimensional Data","authors":"E. J. Nam, Yiping Han, K. Mueller, A. Zelenyuk, D. Imre","doi":"10.1109/VAST.2007.4388999","DOIUrl":"https://doi.org/10.1109/VAST.2007.4388999","url":null,"abstract":"Cluster analysis (CA) is a powerful strategy for the exploration of high-dimensional data in the absence of a-priori hypotheses or data classification models, and the results of CA can then be used to form such models. But even though formal models and classification rules may not exist in these data exploration scenarios, domain scientists and experts generally have a vast amount of non-compiled knowledge and intuition that they can bring to bear in this effort. In CA, there are various popular mechanisms to generate the clusters, however, the results from their non- supervised deployment rarely fully agree with this expert knowledge and intuition. To this end, our paper describes a comprehensive and intuitive framework to aid scientists in the derivation of classification hierarchies in CA, using k-means as the overall clustering engine, but allowing them to tune its parameters interactively based on a non-distorted compact visual presentation of the inherent characteristics of the data in high- dimensional space. These include cluster geometry, composition, spatial relations to neighbors, and others. In essence, we provide all the tools necessary for a high-dimensional activity we call cluster sculpting, and the evolving hierarchy can then be viewed in a space-efficient radial dendrogram. We demonstrate our system in the context of the mining and classification of a large collection of millions of data items of aerosol mass spectra, but our framework readily applies to any high-dimensional CA scenario.","PeriodicalId":227910,"journal":{"name":"2007 IEEE Symposium on Visual Analytics Science and Technology","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125332752","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: 78

VisPad: Integrating Visualization, Navigation and Synthesis VisPad:集成可视化，导航和综合

2007 IEEE Symposium on Visual Analytics Science and Technology Pub Date : 2007-10-30 DOI: 10.1109/VAST.2007.4389021

Y. Shrinivasan, J. V. Wijk

引用次数: 4