I/UCRC FRP: Collaborative Research: Fundamental Research in Visualization-based Gap Analysis and Link Prediction

I/UCRC FRP：合作研究：基于可视化的差距分析和链路预测的基础研究

• 批准号: 1332024
• 负责人: Xiaohua Hu
• 金额: $ 9.99万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1332024&HistoricalAwards=false
• 关键词: UCRC; FRP; Collaborative; Research; Fundamental

The proposed Visualization-Based Gap Analysis effort is aimed at providing an intuitive visualization and analysis techniques to provide analysts with the ability to understand what has happened within a domain, comprehend its current status and operations, and explore the impact of changes to the system. LinkDiscovery is seeking to automatically predict what relations arise in the future between objects. The proposed research intends to reformulate the link discovery problem to include predicting the strength of the link as well as create techniques to detect and predict special categories of future links; such links that arise that bridge two dense clusters of objects. Link prediction for domains other than social media and specialized literature domains will be explored.Predictive analysis to determine future events in an industry environment based on current and past data represents an outcome of tremendous economic and societal impact. Through establishment of more rigorous capabilities for Visual Gap Analysis and Link Discovery, industry may be better able to understand what has happened, comprehend what is the current state, and obtain some grasp of what is likely to happen, may be greatly enhanced increasing productivity and allowing focus on problem resolution. The proposed effort is supported by the Industry Advisory Board and will engage individual center members. Graduate students will be trained through the research and results will be integrated into courses and industry seminars and short courses.

拟议的基于可视化的差距分析工作旨在提供直观的可视化和分析技术，使分析人员能够了解某个领域内发生了什么，了解其当前状态和操作，并探索更改对系统的影响。LinkDiscovery正在寻求自动预测对象之间未来出现的关系。拟议的研究旨在重新制定链接发现问题，包括预测链接的强度，以及创建技术来检测和预测未来链接的特殊类别;这种链接出现，桥接两个密集的对象集群。本课程将探讨除社交媒体和专业文献领域之外的其他领域的链接预测。根据当前和过去的数据进行预测分析，以确定行业环境中的未来事件，这代表了巨大的经济和社会影响的结果。通过建立更严格的视觉差距分析和链接发现能力，行业可以更好地理解发生了什么，理解当前状态是什么，并掌握可能发生的事情，可以大大提高生产力并允许专注于问题解决方案。拟议的努力得到了行业咨询委员会的支持，并将吸引个别中心成员。 研究生将通过研究进行培训，研究结果将融入课程和行业研讨会和短期课程。

项目成果

How Many Ways to Use CiteSpace? A Study of User Interactive Events Over 14 Months

• DOI: 10.1002/asi.23770
• 发表时间: 2017-05-01
• 期刊: JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY
• 影响因子: 3.5
• 作者: Ping, Qing;He, Jiangen;Chen, Chaomei
• 通讯作者: Chen, Chaomei