GV: EAGER: Navigation, Exploration and Visualization Tools for Knowledge Discovery in High Dimensional Data Spaces

GV：EAGER：高维数据空间知识发现的导航、探索和可视化工具

• 批准号: 1050477
• 负责人: Klaus Mueller
• 金额: $ 10.3万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1050477&HistoricalAwards=false
• 关键词: GV; EAGER; Navigation; Exploration; Visualization

Very few real-life phenomena are ever as simple as A causes B ? a bivariate relationship. Take for example economic forecasting: it is a function of unemployment, consumer confidence, inflation, interest rates, and many other factors. There is not one single variable that can solely predict the state of the economy in the next few months. Similar is true in the study of global warming, in the derivation of gene interactions, in the analysis of customer recommendation systems, and so on. Multivariate relationships are ubiquitous and they have always existed. However, with the growth in sensor technology, whatever the data collection mechanism might be (electronic media, physical devices, etc.); we now have a wealth of data available to study in many domains, small and large. Currently, automated and unsupervised methods often fail once the number of variables (dimensions) grows beyond a dozen or even less; hence visualization techniques for user-assisted analysis play an important role. Responding to this need, this exploratory project develops a novel framework that makes high-dimensional (multivariate) data visualization more accessible to all. It couples powerful data analysis with an intuitive exploration and way-finding paradigm ? akin to a tourist map ? to help users navigate high-dimensional data spaces with ease. The overall goal of the project is to facilitate intuitive navigation and exploration of high-dimensional data spaces, improving comprehensibility and reducing unnecessary complexity. This is achieved by: (1) unrolling the high-dimensional space into a landscape map; (2) enabling users to navigate the map and local subspaces of the data via an interactive data projection utility controlled by a touchpad interface; (3) allowing users to insert interesting observations (i.e., data projections) into this map; (4) augmenting the map with background overlays depicting informative globally defined data; and (5) conveying the data within a level-of-detail illustrative visualization framework. The system is evaluated and refined via formal user studies, both with domain scientists in interviews and in a crowd-sourced setting over the web.This novel information visualization approach will provide support to both scientists and casual users to explore high dimensional data spaces in an intuitive navigation paradigm. The project webpage (http://www.cs.sunysb.edu/~mueller/TripAdvisorND) will be used for results dissemination, including data analysis capabilities within a web-enabled version of the software and also used to invite to participation in evaluation studies. This exploratory research project provides a rich research and educational experience to students.

很少有现实生活中的现象像A导致B那样简单。二元关系。以经济预测为例：它是失业率、消费者信心、通货膨胀、利率和许多其他因素的函数。没有一个单一的变量可以完全预测未来几个月的经济状况。在全球变暖的研究中，在基因相互作用的推导中，在客户推荐系统的分析中，等等，也是如此。多元关系无处不在，而且一直存在。然而，随着传感器技术的发展，无论数据收集机制是什么（电子媒体、物理设备等）；我们现在有大量的数据可用于研究许多大大小小的领域。目前，一旦变量（维度）的数量超过十几个甚至更少，自动化和无监督的方法往往会失败；因此，可视化技术在用户辅助分析中起着重要的作用。为了满足这一需求，这个探索性项目开发了一个新颖的框架，使所有人都能更容易地访问高维（多变量）数据可视化。它将强大的数据分析与直观的探索和寻路范例结合在一起。类似于旅游地图？帮助用户轻松浏览高维数据空间。该项目的总体目标是促进对高维数据空间的直观导航和探索，提高可理解性并减少不必要的复杂性。这是通过以下方式实现的：(1)将高维空间展开成景观地图；(2)使用户能够通过由触摸板界面控制的交互式数据投影实用程序来导航数据的地图和局部子空间；(3)允许用户在地图中插入有趣的观察结果（即数据投影）；(4)用背景覆盖来增强地图，描绘信息丰富的全球定义数据；以及(5)在细节级说明性可视化框架内传递所述数据。该系统通过正式的用户研究进行评估和改进，包括领域科学家的访谈和网络上的众包设置。这种新颖的信息可视化方法将为科学家和普通用户提供支持，以直观的导航范式探索高维数据空间。项目网页（http://www.cs.sunysb.edu/~mueller/TripAdvisorND）将用于传播结果，包括在该软件的网络版本中提供数据分析能力，并用于邀请参与评价研究。这个探索性的研究项目为学生提供了丰富的研究和教育经验。