G&V: Medium: Collaborative Research: Large Data Visualization Using An Interactive Machine Learning Framework

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• 批准号: 1065107
• 负责人: David Thompson
• 金额: $ 28.03万
• 依托单位: Mississippi State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-01 至 2015-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1065107&HistoricalAwards=false
• 关键词: G& Medium; Collaborative; Research; Large

Abstract - Machiraju, Rangarajan, and ThompsonAs computer power continues to increase, the complexity of simulations also increases thereby producing datasets of unprecedented size. Without effective analysis tools, results from these large-scale simulations cannot be utilized to their fullest extent. This research addresses the problem of large-data visualization and exploration by employing interactive multi-scale machine learning, which exploits an efficient feature-based, multi-resolution representation of the data. The investigators are leveraging methods from the field of machine learning to perform two distinct tasks: identify regions of interest and enhance robustness of feature detection algorithms. The primary outcome of this effort is the realization of a framework for exploring large datasets. Further, this work is introducing a large body of work in machine learning to the field of visualization. Successful completion of this research will help overcome the brittleness of existing visualization methods and foster expedient discovery in many areas of science and engineering.The multi-resolution techniques developed here will employ a two-fold strategy. First, semi-supervised learning based on training with the domain expert is used to develop strategies for selective spatial and temporal refinement of the data. A classifier is constructed to tag the output of the coarse resolution feature detection (i.e. regions) as either interesting or not interesting. Then at the finest scale, interesting local data chunks containing features of interest are identified for further analysis. Second, several local feature detection algorithms, or weak classifiers, are combined into a single, more robust compound classifier using adaptive boosting, or AdaBoost, and a data adaptive variant called CAVIAR that facilitates validated feature detection. Ideally, the compound classifier combines the best of all weak classifiers as they respond to the underlying physical signal. This research is demonstrating the effectiveness of these methods by applying existing local detection algorithms for visualizing vortices in turbulent flow fields.

摘要 - Machiraju、Rangarajan 和 Thompson 随着计算机能力的不断增强，模拟的复杂性也随之增加，从而产生了前所未有的规模的数据集。 如果没有有效的分析工具，这些大规模模拟的结果就无法得到充分利用。这项研究通过采用交互式多尺度机器学习来解决大数据可视化和探索问题，该学习利用了基于特征的高效数据多分辨率表示。研究人员正在利用机器学习领域的方法来执行两项不同的任务：识别感兴趣的区域并增强特征检测算法的鲁棒性。这项工作的主要成果是实现了探索大型数据集的框架。此外，这项工作将机器学习的大量工作引入可视化领域。这项研究的成功完成将有助于克服现有可视化方法的脆弱性，并促进科学和工程许多领域的便利发现。这里开发的多分辨率技术将采用双重策略。首先，基于领域专家训练的半监督学习用于开发选择性空间和时间细化数据的策略。 构建分类器以将粗分辨率特征检测的输出（即区域）标记为感兴趣或不感兴趣。然后，以最精细的规模，识别包含感兴趣特征的有趣本地数据块以进行进一步分析。其次，使用自适应增强 (AdaBoost) 和名为 CAVIAR 的数据自适应变体（有助于验证特征检测）将多个局部特征检测算法（或弱分类器）组合成一个更鲁棒的复合分类器。理想情况下，复合分类器结合了所有弱分类器中最好的，因为它们响应潜在的物理信号。这项研究通过应用现有的局部检测算法来可视化湍流场中的涡流，证明了这些方法的有效性。