Integrated Visualization of Multiple Data Streams for Command Control Interfaces (CCI)

可视化是一种通用的支持技术，但是大多数可视化系统的设计都不能适应许多实际应用程序中动态输入数据流的复杂性。例如，在一个典型的交通控制中心，有许多不同的数据流，例如来自交通摄像头的视频流、从GPS设备收集的部分交通信息、通过无线电和紧急服务提供的事故报告、卫星图像（例如谷歌earth）、传统地图以及音乐会和体育赛事等其他相关信息。然而，这些数据流通常是分开显示的。交通管制员必须同时从不同的显示器上收集信息，并在脑海中建立一个关于全球形势的图像。尽管有一些地理信息系统可以使用一种称为地图覆盖的经典技术来显示多张地图，但在交通控制中心等应用中使用这种机制的情况很少。部分原因是地理信息系统通常依赖于少数视觉渠道，如彩色线条、斑块和文本标签。不同的映射很容易使用相似的可视化映射，从而在组合的可视化中产生冲突。此外，时间数据（如视频和时间序列）在许多应用程序中变得越来越普遍。这些数据的描述通常以动画、跟踪线和热图的形式（使用颜色来显示变化的程度）。跟踪线和热图通常分别与底层地理地图和卫星图像相冲突，而动画存在许多感知和认知缺陷，不适合连续监测和客观评价。例如，看动画需要时间和全部注意力。如果交通管制员正在观看先前记录的事件展开，他/她无法关注当前情况。因此，挑战在于如何在不使用“时间”（即视频或动画）的情况下将“时间”（即时间数据）可视化。在这个项目中，我们将开发两种新技术。我们将增加可用于多流可视化的视觉通道的数量和类型，同时开发一套冲突诊断设施，该设施通过算法测量由于与不同层相关的冲突视觉映射而导致的质量缺陷。诊断设备可以对用户发出警告，因为视觉通道的冲突使用可能导致混淆和误解。此外，我们将设计一套新的时间可视化形式，使用户能够在不使用“时间”的情况下可视化“时间”。我们将开发一个演示系统，其中不同的数据流可以插入和播放。该系统将由一个仪表盘支持，运行在iPad等平板电脑上。通过仪表板，用户可以激活和停用单个数据流，选择合适的可视化通道，接收冲突诊断设施的建议，控制各层的外观，管理不同层的顺序。我们将在两个应用中评估演示系统，弹性风险可视化和考古数据可视化。

Visualization is generic enabling technology, but most visualization systems have not been designed to accommodate the complexity of dynamic input data streams in many real world applications. For example, in a typical traffic control centre, there are many different data streams, such as video streams from traffic cameras, partial traffic information collected from GPS devices, accident reports via radio and emergency services, satellite images (e.g., Google earth), traditional maps, and other related information such as concerts and sports events. However, these data streams are usually displayed separately. Traffic controllers have to gather information from different displays simultaneously and build a mental image about the global situation.Although there are geographical information systems that can display multiple maps using a classic technique called map overlay, the use of such a mechanism in applications such as traffic control centre, is rare. This is partly because that geographical information systems typically rely on a small number of visual channels, such as colour lines, patches, and text labels. It is rather easy for different maps to use similar visual mappings, creating conflicts in the combined visualization. Furthermore, temporal data (such as videos and time series) are becoming common in many applications. The depiction of such data is typically in the form of animation, tracking lines and heatmap (using colour to show the level of changes). Tracking lines and heatmaps are typically in conflict with the underlying geographic maps and satellite images respectively, while animation suffers from a number of perceptual and cognitive shortcomings and is not suitable for continuous monitoring and objective evaluation. For example, watching animation requires time and full attention. If a traffic controller is watching a previously-recorded event unfolding, he/she cannot pay attention to the current situation. So the challenge is how can one visualize "time" (i.e., temporal data) without using "time" (i.e., video or animation).In this project, we will develop two pieces of novel techniques. We will increase the number and types of the visual channels that can be used in multi-stream visualization, while develop a set of conflict diagnostic facilities, which algorithmically measure the quality deficiency due to conflict visual mappings associated with different layers. The diagnostic facilities can give warnings to the users as the potential risks of confusion and misunderstanding caused of conflict use of visual channels. In addition, we will design a set of new forms of temporal visualization that enable uses to visualize "time" without using "time".We will develop a demo system, where different data streams can be plugged-in and play. The system will be supported by a dashboard, running on a tablet computer such as iPad. Through the dashboard, the users can activate and deactivate individual data streams, select appropriate visual channels, receive advice from the conflict diagnostic facilities, control the appearance of each layers, and manger the order of different layers. We will evaluate the demo system in two applications, risk visualization in resilience and archeological data visualization.