Quality-Aware Visual Exploration Tools

可视化已被确定为复杂数据库的交互式探索和挖掘过程中的一个关键组成部分。这个项目的重点是在视觉探索过程的各个阶段的质量问题。所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;将数据转换为汇总数据摘要的质量;通过控制检索数据的服务质量来提高数据查询过程的质量;以及数据属性到图形对象及其屏幕布局的映射质量，从而导致不同的可感知结构和各种程度的混乱。该项目打破了在整个可视化探索管道中处理质量的整体方法，在每个阶段开发：（1）用于测量和捕获质量的模型和模型;（2）基于选定的质量度量的质量优化算法;（3）传达数据和结构空间的质量信息的可视化;和（4）交互式工具，使用户能够控制优化过程，并在存在权衡时指定偏好。智力价值涉及定义，计算，优化，以及在可视化流水线的所有阶段处的信息内容和结构空间两者的质量度量和属性的可视呈现。预期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明确地暴露和集成数据的质量来实现有效的可视化探索，而这些数据在现有工具中往往被忽视，但会对决策过程产生巨大影响。该项目的成果将通过因特网（http：//davis.wpi.edu/jpxmdv）传播。

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).