Quality-Aware Visual Exploration Tools

质量意识视觉探索工具

• 批准号: 0414380
• 负责人: Matthew Ward
• 金额: --
• 依托单位: Worcester Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0414380&HistoricalAwards=false
• 关键词: Quality; Aware; Visual; Exploration; Tools

Visualization has been identified as a critical component in the process of interactive exploration and mining of complex data repositories. This project focuses on the issue of quality at all stages of the visual exploration process. Types of quality considered include quality of the data itself, including reliability, age and completeness; quality of the transformation of the data into aggregated data abstraction; quality of data querying process by controlling of quality-of-service of retrieved data; and quality of mapping of data attributes to graphical objects and their screen layout resulting in different perceptible structures and various degrees of clutter.To achieve a ground-breaking holistic approach towards handling quality across the visual exploration pipeline, this project develops at each stage: (1) Metrics and models for measuring and capturing quality; (2) Algorithms for quality optimization based on selected quality metrics; (3) Visualizations to convey quality information for both data and structure spaces; and (4) Interactive tools to enable the user to control the optimization processes and specify preferences when trade-offs exist.Intellectual merit involves the definition, calculation, optimization, and visual presentation of quality metrics and attributes of both the information content and the structure space at all stages of the visualization pipeline. The expected results will have an impact in the application domains that routinely perform exploratory data analysis. The resulting tools enable effective visual exploration by explicitly exposing and integrating the quality of the data, which, while often ignored in existing tools, can have a tremendous impact on the decision-making process. The results of the project will be disseminated via the Internet (http://davis.wpi.edu/~xmdv).

可视化已被确定为复杂数据存储库交互式探索和挖掘过程中的关键组成部分。这个项目关注的是视觉探索过程中各个阶段的质量问题。考虑的质量类型包括数据本身的质量，包括可靠性、年龄和完整性；数据转化为聚合数据抽象的质量；通过对检索数据服务质量的控制来提高数据查询过程的质量数据属性映射到图形对象及其屏幕布局的质量导致不同的可感知结构和不同程度的杂乱。为了在整个视觉勘探管道中实现突破性的整体处理质量方法，该项目在每个阶段进行开发：(1)测量和捕获质量的度量和模型；(2)基于选定质量指标的质量优化算法；(3)可视化以传递数据和结构空间的高质量信息；(4)交互式工具，使用户能够控制优化过程，并在权衡存在时指定偏好。智力价值包括在可视化管道的所有阶段对信息内容和结构空间的质量度量和属性的定义、计算、优化和可视化表示。预期的结果将对常规执行探索性数据分析的应用程序领域产生影响。由此产生的工具通过显式地暴露和集成数据的质量来实现有效的视觉探索，这在现有工具中经常被忽视，但对决策过程有巨大的影响。该项目的结果将通过互联网（http://davis.wpi.edu/~xmdv）传播。