Woodring Jonathan, Shen Han-Wei
Ohio State University, Columbus, OH 43210, USA.
IEEE Trans Vis Comput Graph. 2009 Jan-Feb;15(1):123-37. doi: 10.1109/TVCG.2008.69.
Time-varying data is usually explored by animation or arrays of static images. Neither is particularly effective for classifying data by different temporal activities. Important temporal trends can be missed due to the lack of ability to find them with current visualization methods. In this paper, we propose a method to explore data at different temporal resolutions to discover and highlight data based upon time-varying trends. Using the wavelet transform along the time axis, we transform data points into multi-scale time series curve sets. The time curves are clustered so that data of similar activity are grouped together, at different temporal resolutions. The data are displayed to the user in a global time view spreadsheet where she is able to select temporal clusters of data points, and filter and brush data across temporal scales. With our method, a user can interact with data based on time activities and create expressive visualizations.
时变数据通常通过动画或静态图像数组进行探索。对于按不同时间活动对数据进行分类，这两种方法都不是特别有效。由于当前可视化方法缺乏发现重要时间趋势的能力，这些趋势可能会被遗漏。在本文中，我们提出了一种方法，以不同的时间分辨率探索数据，从而根据时变趋势发现并突出显示数据。沿着时间轴使用小波变换，我们将数据点转换为多尺度时间序列曲线集。对时间曲线进行聚类，以便在不同的时间分辨率下将具有相似活动的数据分组在一起。数据在全局时间视图电子表格中显示给用户，用户能够选择数据点的时间聚类，并跨时间尺度过滤和筛选数据。使用我们的方法，用户可以基于时间活动与数据进行交互，并创建富有表现力的可视化效果。
