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

查询驱动可视化（Query-Driven Visualization， 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的重要记录上，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，数据和查询中的不确定性会对可视化结果产生强烈的负面影响，从而影响从中获得的洞察力。本研究项目探索了一个概括QDV的新框架，旨在解决现有方法的不足。提供健壮的可视化技术的方法，将分析过程的不确定性结合到可视化结果中，从而使工具能够允许用户将不确定性因素考虑到从数据集得出的结论中，这种方法基于查询驱动过程的所有级别的不确定性建模。所开发的方法利用包含不确定性信息的多分辨率数据表示；在回答非常大的、高维数据集的查询时，这将提高效率和并行计算。新的可视化技术是通过利用所提供框架的改进的灵活性、通用性和效率而衍生出来的，以专门解决与共同科学问题有关的数据集集合的比较可视化的需要。由此产生的强大的查询驱动可视化技术，将知识发现过程中分析的不确定性结合起来，将允许用户将不确定性因素考虑到从复杂、大规模、高维数据集得出的结论中。为了增加研究的影响，结果将通过项目网站（http://idav.ucdavis.edu/~joy/NSF-IIS-1018097.html）访问，并纳入开源可视化包。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.