2007 IEEE Symposium on Visual Analytics Science and Technology最新文献_第2页

Analyzing Large-Scale News Video Databases to Support Knowledge Visualization and Intuitive Retrieval 分析支持知识可视化和直观检索的大型新闻视频数据库

2007 IEEE Symposium on Visual Analytics Science and Technology Pub Date : 2007-10-30 DOI: 10.1109/VAST.2007.4389003

Hangzai Luo, Jianping Fan, Jing Yang, W. Ribarsky, S. Satoh

引用次数: 23

Situation Awareness Tool for Global Argus 全球阿格斯的态势感知工具

2007 IEEE Symposium on Visual Analytics Science and Technology Pub Date : 2007-10-30 DOI: 10.1109/VAST.2007.4389023

Jae Choi, Sang-joon Lee, Sarah Gigitashvilli, V. JamesM.Wilson

引用次数: 3

IMAS: The Interactive Multigenomic Analysis System 交互式多基因组分析系统

2007 IEEE Symposium on Visual Analytics Science and Technology Pub Date : 2007-10-30 DOI: 10.1109/VAST.2007.4388997

C. Shaw, G. Dasch, M. Eremeeva

引用次数: 10

VAST 2007 Contest - Analysis with nSpace and GeoTime VAST 2007竞赛-使用nSpace和GeoTime进行分析

2007 IEEE Symposium on Visual Analytics Science and Technology Pub Date : 2007-10-30 DOI: 10.1109/VAST.2007.4389033

Lynn Chien, A. Tat, T. Kapler, P. Enns, Winniefried Kuan, W. Wright

引用次数: 0

University of British Columbia & Simon Fraser University - The Bricolage 英属哥伦比亚大学和西蒙弗雷泽大学-拼贴

2007 IEEE Symposium on Visual Analytics Science and Technology Pub Date : 2007-10-30 DOI: 10.1109/VAST.2007.4470207

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

引用次数: 0

LAHVA: Linked Animal-Human Health Visual Analytics 链接动物-人类健康可视化分析

2007 IEEE Symposium on Visual Analytics Science and Technology Pub Date : 2007-10-30 DOI: 10.1109/VAST.2007.4388993

Ross Maciejewski, Benjamin Tyner, Yun Jang, Cheng Zheng, Rimma V. Nehme, D. Ebert, W. Cleveland, M. Ouzzani, S. Grannis, L. Glickman

{"title":"LAHVA: Linked Animal-Human Health Visual Analytics","authors":"Ross Maciejewski, Benjamin Tyner, Yun Jang, Cheng Zheng, Rimma V. Nehme, D. Ebert, W. Cleveland, M. Ouzzani, S. Grannis, L. Glickman","doi":"10.1109/VAST.2007.4388993","DOIUrl":"https://doi.org/10.1109/VAST.2007.4388993","url":null,"abstract":"Coordinated animal-human health monitoring can provide an early warning system with fewer false alarms for naturally occurring disease outbreaks, as well as biological, chemical and environmental incidents. This monitoring requires the integration and analysis of multi-field, multi-scale and multi-source data sets. In order to better understand these data sets, models and measurements at different resolutions must be analyzed. To facilitate these investigations, we have created an application to provide a visual analytics framework for analyzing both human emergency room data and veterinary hospital data. Our integrated visual analytic tool links temporally varying geospatial visualization of animal and human patient health information with advanced statistical analysis of these multi-source data. Various statistical analysis techniques have been applied in conjunction with a spatio-temporal viewing window. Such an application provides researchers with the ability to visually search the data for clusters in both a statistical model view and a spatio-temporal view. Our interface provides a factor specification/filtering component to allow exploration of causal factors and spread patterns. In this paper, we will discuss the application of our linked animal-human visual analytics (LAHVA) tool to two specific case studies. The first case study is the effect of seasonal influenza and its correlation with different companion animals (e.g., cats, dogs) syndromes. Here we use data from the Indiana Network for Patient Care (INPC) and Banfield Pet Hospitals in an attempt to determine if there are correlations between respiratory syndromes representing the onset of seasonal influenza in humans and general respiratory syndromes in cats and dogs. Our second case study examines the effect of the release of industrial wastewater in a community through companion animal surveillance.","PeriodicalId":227910,"journal":{"name":"2007 IEEE Symposium on Visual Analytics Science and Technology","volume":"37 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":"124418670","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: 31

SpiralView: Towards Security Policies Assessment through Visual Correlation of Network Resources with Evolution of Alarms 螺旋视图:基于网络资源可视化关联与告警演化的安全策略评估

2007 IEEE Symposium on Visual Analytics Science and Technology Pub Date : 2007-10-30 DOI: 10.1109/VAST.2007.4389007

E. Bertini, Patrick Hertzog, D. Lalanne

{"title":"SpiralView: Towards Security Policies Assessment through Visual Correlation of Network Resources with Evolution of Alarms","authors":"E. Bertini, Patrick Hertzog, D. Lalanne","doi":"10.1109/VAST.2007.4389007","DOIUrl":"https://doi.org/10.1109/VAST.2007.4389007","url":null,"abstract":"This article presents SpiralView, a visualization tool for helping system administrators to assess network policies. The tool is meant to be a complementary support to the routine activity of network monitoring, enabling a retrospective view on the alarms generated during and extended period of time. The tool permits to reason about how alarms distribute over time and how they correlate with network resources (e.g., users, IPs, applications, etc.), supporting the analysts in understanding how the network evolves and thus in devising new security policies for the future. The spiral visualization plots alarms in time, and, coupled with interactive bar charts and a users/applications graph view, is used to present network data and perform queries. The user is able to segment the data in meaningful subsets, zoom on specific related information, and inspect for relationships between alarms, users, and applications. In designing the visualizations and their interaction, and through tests with security experts, several ameliorations over the standard techniques have been provided.","PeriodicalId":227910,"journal":{"name":"2007 IEEE Symposium on Visual Analytics Science and Technology","volume":"110 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":"114251106","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: 48

VAST 2007 Contest - Blue Iguanodon 浩瀚2007大赛-蓝色禽龙

2007 IEEE Symposium on Visual Analytics Science and Technology Pub Date : 2007-10-30 DOI: 10.1109/VAST.2007.4389032

G. Grinstein, C. Plaisant, S. Laskowski, T. O'Connell, J. Scholtz, M. Whiting

引用次数: 15

Sunfall: A Collaborative Visual Analytics System for Astrophysics Sunfall:天体物理学的协同可视化分析系统

2007 IEEE Symposium on Visual Analytics Science and Technology Pub Date : 2007-10-30 DOI: 10.1109/VAST.2007.4389026

C. Aragon, Stephen Bailey, S. Poon, K. Runge, R. Thomas

引用次数: 19

C-GROUP: A Visual Analytic Tool for Pairwise Analysis of Dynamic Group Membership C-GROUP:动态组成员关系成对分析的可视化分析工具

2007 IEEE Symposium on Visual Analytics Science and Technology Pub Date : 2007-10-30 DOI: 10.1109/VAST.2007.4389022

Hyunmo Kang, L. Getoor, L. Singh

{"title":"C-GROUP: A Visual Analytic Tool for Pairwise Analysis of Dynamic Group Membership","authors":"Hyunmo Kang, L. Getoor, L. Singh","doi":"10.1109/VAST.2007.4389022","DOIUrl":"https://doi.org/10.1109/VAST.2007.4389022","url":null,"abstract":"C-GROUP is a tool for analyzing dynamic group membership in social networks over time. Unlike most network visualization tools, which show the group structure within an entire network, or the group membership for a single actor, C-GROUP allows users to focus their analysis on a pair of individuals of interest. And unlike most dynamic social network visualization tools, which focus on the addition and deletion of nodes (actors) and edges (relationships) over time, C-GROUP focuses on changing group memberships over time. C-GROUP provides users with a flexible interface for defining (and redefining) groups interactively, and allows users to view the changing group memberships for the pair over time. This helps to highlight the similarities and differences between the individuals and their evolving group memberships. C-GROUP allows users to dynamically select the time granularity of the temporal evolution and supports two novel visual representations of the evolving group memberships. This flexibility gives users alternate views that are appropriate for different network sizes and provides users with different insights into the grouping behavior.","PeriodicalId":227910,"journal":{"name":"2007 IEEE Symposium on Visual Analytics Science and Technology","volume":"10 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":"122472241","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