CGV: Small: Graph-Based Techniques for Visual Analytics of Big Scientific Data

科学可视化已经成为对各种领域的模拟和实验产生的数据进行可视化分析的不可或缺的工具。尽管在开发处理、管理和呈现科学数据集的新算法和技术方面取得了实质性进展，但仍存在一些关键挑战。这些挑战包括在可视化大型三维标量和矢量场时解决固有的遮挡和杂波问题，检查复杂的数据关系并跟踪其随时间变化的多变量数据，以及获得全面的概述并获得数据导航的完全控制以收集关键见解。不断增长的数据规模和复杂性只会加剧这些挑战。为了实现大数据的发现，需要超越科学可视化的传统边界，寻求数据抽象和关系探索的新视角，充分利用信息可视化技术进行有效的可视化数据分析。虽然有一些令人鼓舞的孤立的例子将信息可视化技术（如平行坐标和树状图）应用于科学数据分析，但利用更普遍和熟悉的图形形式在更大程度上解决更广泛的科学可视化问题尚未得到充分研究。在这个项目中，pi的目标是建立系统的基于图形的技术来调查大规模的标量和矢量科学数据集。为此，该团队开展了三个主要任务：(1)探索基于图的核心技术来分析和探索时变多变量标量场和向量场数据；(2)开发可扩展的并行算法，用于构建和可视化大型图形，以实现科学可视化；(3)使用选择行为模型进行形式化的用户研究，并通过专家评估解决应用领域的实际问题。由于科学可视化在许多科学、工程和医学领域发挥着关键作用，因此基于图形的通用可视化分析工具的潜在好处是深远的。所开发的一般思想将直接有利于理解体积科学数据集，包括标量和矢量场数据，时变和多变量数据。它们还将影响对其他形式数据的理解，如自适应网格细化、非结构化网格和基于点的数据。由于这项工作是对传统方法的背离，它可以通过提供一种全新的探索和分析大科学数据的方式来实现变革。从科学的角度来看，潜在的影响是一种新的知识发现技术。该项目将通过与燃烧和生物医学科学家的密切合作，最大限度地提高其成果。它将产生各种形式的结果，并在项目网站（http://www.nd.edu/~cwang11/nsf13-graph.htm）上公布。该项目为研究生、本科生和代表性不足的学生提供大数据计算和可视化方面的培训。公共宣传活动已经计划好了，包括针对中学生和高中生的暑期项目，以及在顶级可视化会议上为研究人员提供的教程或竞赛。

Scientific visualization has become an indispensable tool for visual analysis of data generated from simulations and experiments across a wide variety of fields. Although there have been substantial advances in developing novel algorithms and techniques for processing, managing and rendering scientific datasets, several critical challenges still remain. These challenges include solving the inherent occlusion and clutter problem when visualizing large three-dimensional scalar and vector fields, examining complex data relationships and tracking their changes over time for time-varying multivariate data, and gaining a comprehensive overview and acquiring full control of data navigation to glean critical insights. The ever-growing size and complexity of data produced only exacerbate these challenges. To enable discovery from big scientific data, there is a need to seek a new perspective on data abstraction and relationship exploration by going beyond the traditional boundary of scientific visualization and fully incorporating information visualization techniques for effective visual data analytics. While there are encouraging, isolated examples of applying information visualization techniques such as parallel coordinates and treemaps to scientific data analysis, leveraging the more generalized and familiar form of graphs to address a wider range of scientific visualization problems at greater extent has not been fully studied. In this project, the PIs' goal is to establish systematic graph-based techniques to investigate large-scale scalar and vector scientific datasets. To this end the team pursues three major tasks: (1) exploring core graph-based techniques to analyze and explore time-varying multivariate scalar and vector field data; (2) developing scalable parallel algorithms for constructing and visualizing large graphs for scientific visualization; and (3) conducting a formal user study using the choice behavior model and tackling real problems from application domains with expert evaluation.Because scientific visualization plays a key role in many scientific, engineering and medical fields, the potential benefits from generalized graph-based visual analytics tools are far reaching. The general ideas developed will directly benefit the understanding of volumetric scientific datasets including scalar and vector field data, time-varying and multivariate data. They will also impact the understanding of data in other forms such as adaptive mesh refinement, unstructured grid and point-based data. Since this work is a departure from traditional approaches, it could be transformative by providing a completely new way of exploring and analyzing big scientific data. From a scientific perspective, the potential impact is a new class of techniques for knowledge discovery. This project will maximize its outcomes through close collaboration with combustion and biomedical scientists. It will produce results in various forms which are publicized at the project website (http://www.nd.edu/~cwang11/nsf13-graph.htm). The project provides training for graduate, undergraduate and under-represented students in big data computing and visualization. Public outreach activities are planned, including summer programs for middle and high school students, and tutorials or contests for researchers at premier visualization conferences.