III/G&V: Small: Enhanced Query-Driven Techniques for Uncertainty and Comparative Visualization

查询驱动可视化（QDV）是一种知识发现策略，它将科学数据管理的最先进方法与现代可视化方法相结合，以支持快速数据分析。通过将可视化和解释中的计算和认知工作量限制在科学家定义为重要的记录中，快速可视化响应可以回答有关数据的直观问题。因此，查询驱动技术是数据探索和假设检验的理想工具。然而，在数据和查询的不确定性可以强烈和负面影响的可视化结果，因此从它获得的洞察力。本研究项目探讨了一种新的框架，概括QDV，旨在解决现有方法的不足。提供强大的可视化技术，将分析过程的不确定性纳入可视化结果，从而使工具，将允许用户因素的不确定性从数据集得出的结论的方法是基于建模的不确定性在所有级别的查询驱动的过程。开发的方法利用多分辨率的数据表示，将不确定性信息，这导致在回答查询非常大，高维数据集的效率和并行计算。新的可视化技术是通过利用所提供的框架的改进的灵活性，通用性和效率的优势，以具体解决有关一个共同的科学问题的数据集的集合的比较可视化的需求。由此产生的强大的查询驱动的可视化技术，将在知识发现过程中的分析的不确定性，将允许用户因素的不确定性从复杂的，大规模的，高维的数据集得出的结论。为了扩大研究的影响，研究结果将通过项目网站（http：//idav.ucdavis.edu/joy/NSF-IIS-1018097.html）提供，并纳入一个开放源码可视化软件包。项目为学生提供研究经验。

Query-Driven Visualization (QDV) is a knowledge discovery strategy that combines state-of-the-art methods from scientific data management with modern visualization approaches to support rapid data analysis. By restricting computational and cognitive workload in visualization and interpretation to records defined to be significant by a scientist, fast visualization responses can answer intuitive questions about the data. Thus, query-driven techniques are ideal tools for data exploration and hypothesis testing. However, uncertainty in data and query can strongly and negatively influence a visualization result and hence the insight obtained from it. This research project explores a novel framework that generalizes QDV and is aimed at addressing deficiencies in existing methods. The approach to providing robust visualization techniques that incorporate the uncertain nature of the analysis process into the visualization result, thus enabling tools that will allow users to factor uncertainty into the conclusions drawn from data sets are based on modeling uncertainty at all levels of the query-driven process. The methods developed leverage multi-resolution data representations incorporating uncertainty information; and this results in improved efficiency and parallel computation in answering queries over very large, high-dimensional data sets. New visualization techniques are derived by taking advantage of the improved flexibility, generality and efficiency of the provided framework, to address specifically the needs of comparative visualization of an ensemble of data sets pertaining to a common science problem. The resulting robust query-driven visualization techniques that incorporate the uncertain nature of the analysis in the knowledge discovery process will allow users to factor uncertainty into the conclusions drawn from complex, large-scale, high-dimensional data sets. In order to increase the impact of the research, results will be accessible via the project Web site (http://idav.ucdavis.edu/~joy/NSF-IIS-1018097.html), and incorporated into an open-source visualization package. Project provides research experience to students.