Multiscale time activity data exploration via temporal clustering visualization spreadsheet.
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.
Woodring
Jonathan
J
Ohio State University, Columbus, OH 43210, USA. woodring@cse.ohio-state.edu
Shen
Han-Wei
HW
D000465:Algorithms; D016000:Cluster Analysis; D003196:Computer Graphics; D003198:Computer Simulation; D016247:Information Storage and Retrieval / Q000379:methods*; D008962:Models, Theoretical; D012984:Software; D014584:User-Computer Interface