Spatial input for temporal navigation in scientific visualizations
IEEE Computer Graphics and Applications
Visually defining and querying consistent multi-granular clinical temporal abstractions
Artificial Intelligence in Medicine
Generating time lines with virtual words for time-varying data visualization
Proceedings of the 5th International Symposium on Visual Information Communication and Interaction
Semi-automatic time-series transfer functions via temporal clustering and sequencing
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
Visual exploration of time-series data with shape space projections
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Trace selection for interactive evolutionary algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Visualization and analysis of 3D time-varying simulations with time lines
Journal of Visual Languages and Computing
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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.