VISUALIZATION: A Metadata-Driven Visualization Interface Technology for Scientific Data Exploration

该项目列出了我们的研究和开发计划，旨在极大地改进科学数据分析和可视化的过程和结果。这一改进将通过将可表达和可扩展的元数据管理框架与新的可视化界面结合起来实现，这些界面有助于有效地重复使用、共享和交叉探索可视化信息，从而对广泛的科学应用产生深远的影响。科学可视化的过程本质上是迭代的。良好的可视化效果来自对可视化和渲染参数的实验，以显示数据中最相关的信息。这就提出了一个问题。考虑到我们经常在探索和生产可视化方面投入的计算机和人力时间，是否有方法和机制不仅可以提高科学家的生产力，而且可以提高他们对可视化过程和使用的数据的理解？数据可视化领域的最新进展主要是在渲染和显示技术(如实时体绘制和沉浸式环境)方面取得的进展，但在一致地管理、表示和共享关于可视化过程和结果(图像和洞察力)的信息方面却很少。自然，关于数据探索的各种信息应该被共享和重复使用，以利用科学家从可视化科学数据中获得的知识和经验。数据探索过程的可视化表示，以及用于记录和查询任务特定信息的表达模型，有助于科学家跟踪他们的可视化经验和发现，使用它来生成新的可视化，并与其他人分享。虽然之前的研究已经解决了一些相关问题，但仍有更全面的研究有待完成。因此，我们提出了两条互补的研究途径：(1)用于数据可视化任务的新的用户界面；(2)支持记录和查询与数据探索任务相关的信息的表达性元数据模型。此外，将在实现(1)和(2)的基于Web的可视化试验台上进行一组用户研究，以完善所提出的方法和设计。传统的用户界面不能支持日益复杂的科学数据探索过程。必须对传统的设计和功能进行根本性的改变，以提供更直观的交互、指导和增强的感知。我们将从丰富我们开发的基于图形和类似电子表格的界面开始我们的研究，并调查替代设计。需要一个表示数据探索过程及其嵌入的数据可视化过程的可表达和可扩展的元数据模型。这样的模型连同适当的用户界面使得有可能管理关于可视化过程的输入和结果的各种信息、分析参数覆盖和使用、标识未探索的可视化空间、以及以可视化元数据的形式合并关于过程和结果的发现。该模型与实际使用的可视化界面无关，并且是开放的，因为它以元数据储存库的形式实现可以与各种不同的可视化工具松散地耦合。我们将设计一套到存储库的接口和协议来管理、查询和分析从不同可视化工具收集和使用的可视化元数据。我们的目标雄心勃勃，只有与应用程序科学家密切合作才能实现。它们将帮助我们了解依赖于应用程序和独立的可视化需求、流程和信息。作为回报，我们将为他们提供一种新的、大大改进的方法来理解他们的科学数据，这将帮助他们更快地带来新的发现，可能会降低成本。

This project lays out our plan for research and development aimed atdramatically improving the processand outcome of scientific data analysis and visualization. The improvement will be achieved by coupling an expressive and extensible metadata management framework with novel visualization interfaces that facilitateeffective reuse, sharing, and cross-exploration of visualizationinformation and thus will make a profoundimpact on a broad range of scientific applications. The process of scientific visualization is inherently iterative. A good visualization comes from experimenting with visualization and rendering parameters to bring out the most relevant information in the data.This raises a question. Considering the computer and human time we routinely invest for exploratory andproduction visualization, are there methodologies and mechanisms to enhance not only the productivity ofscientists but also their understanding of the visualization process anddata used?Recent advances in the field of data visualization have been made mainly in rendering and displaytechnologies (such as realtime volume rendering and immersive environments), but little in coherently managing, representing, and sharing information about the visualization processand results (images and insights).Naturally, the various information about data exploration should be shared and reused to leveragethe knowledge and experience scientists gain from visualizing scientific data. A visual representation of thedata exploration process along with expressive models for recording and querying task specific informationhelp scientists keep track of their visualization experience and findings, use it to generate new visualizations, and share it with others.While previous research has addressed some related issues, a more comprehensive study remains to bedone. Thus, we propose two complementary avenues of research: (1) new user interfaces for data visualization tasks, and (2) expressive metadata models supporting the recording andquerying of information related to data exploration tasks. In addition, a set of user studies will beconducted on a Web-based visualization testbed realizing (1) and (2) in order to refine the proposedmethodologies and designs.Traditional user interfaces cannot support the increasingly complex process of scientific data exploration.A fundamental change in the conventional designs and functionality must be made to offer moreintuitive interaction, guidance, and enhanced perception. We will begin our study with enriching the graphbased and spreadsheet-like interfaces we have developed, and also investigate alternative designs. An expressive and extensible metadata model representing the data exploration process and its embedded data visualization process is needed. Such a model along with an appropriate user interface makes it possible to manage diverse information about the input and results of the visualization process, analyze parameter coverage and usage, identify unexplored visualization spaces, and incorporate findings on the process and results in form of visualization metadata. The model is independent of the actual visual interface used and is open in that its realization in form of a metadata repository can be loosely coupled with a variety of different visualization tools. A set of interfaces and protocols to the repository will be designed to manage, query, and analyze visualization metadata gathered from and utilized by different visualization tools.Our goal is ambitious and can only be accomplished by working closely with application scientists.They will help us understand application-dependent and independent visualization requirements, processes,and information. In return, we will offer them a new and greatly improved way to understand their scientificdata, which will help them lead to new discoveries quicker, likely with reduced cost.