CAREER: Visualizing Knowledge Domains

知识领域可视化（KDVs）是一种特殊的信息可视化（IVs），它利用人类强大的视觉和空间认知能力来帮助人类在精神上组织、电子访问和管理大型、复杂的信息空间。与科学可视化不同，kdv是由没有空间参考的数据（如出版物、专利或授权）创建的。该项目的研究部分包括为KDV教育和研究建立一个先进的开放式架构和计算基础设施，以及开发和全面评估增量和可扩展数据分析、降维和空间布局的算法。利用制图原理，方便使用传统上与地理地图有关的技能来探索kdv。教育计划包括开发信息可视化数据和软件存储库，该存储库与相关的学习模块一起为现有和新开发的IV和KDV软件提供公众访问和培训。本研究探讨了知识基础课程在教育方面的价值，例如让学生对教材有一个“大局观”或全球视野。该项目将使KDVs成为一种新的信息访问和管理工具，有可能极大地改善学生、研究人员、机构、研发经理、行业、数据提供者和社会的科学成果交流；(2)为IV/KDV研究人员创建一个先进的计算基础设施，以便在不同的数据集上贡献、运行和比较新的和现有的算法；(3)通过在线IV/KDV存储库、相关学习模块以及教科书来改善IV/KDV的教学、培训和学习。网页：http://vw.indiana.edu/kdv

Knowledge Domain Visualizations (KDVs) are a special kind of Information Visualizations (IVs) that exploit powerful human vision and spatial cognition to help humans mentally organize and electronically access and manage large, complex information spaces. Unlike scientific visualizations, KDVs are created from data that have no spatial reference, such as publications, patents, or grants.The research component of this project comprises the setup of an advanced, open architecture, computing infrastructure for KDV education and research as well as the development and comprehensive evaluation of algorithms for incremental and scalable data analysis, dimensionality reduction, and spatial layout. Cartographic principles are employed to facilitate the use of skills traditionally associated with geographic maps for the exploration of KDVs. The education plan includes the development of an Information Visualization Data and Software Repository that together with associated Learning Modules provides public access to and training for existing and newly developed IV and KDV software. The value of KDVs for educational purposes, e.g., to equip students with a "big picture" or global view of teaching material, is explored.The project will make KDVs available as a new information access and management tool that has the potential to dramatically improve the communication of scientific results for students, researchers, agencies, R&D managers, industry, data providers, and society; (2) create an advanced computing infrastructure for IV/KDV researchers to contribute, run, and compare new and existing algorithms on diverse data sets; and (3) improve IV/KDV teaching, training and learning by means of an online IV/KDV repository, associated learning modules, as well as a textbook. Web page: http://vw.indiana.edu/kdv