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
Visualization of time-series data in parameter space for understanding facial dynamics
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
A gradient-based comparison measure for visual analysis of multifield data
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Visualization and analysis of 3D time-varying simulations with time lines
Journal of Visual Languages and Computing
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We present a new algorithm to explore and visualize multivariate time-varying data sets. We identify important trend relationships among the variables based on how the values of the variables change over time and how those changes are related to each other in different spatial regions and time intervals. The trend relationships can be used to describe the correlation and causal effects among the different variables. To identify the temporal trends from a local region, we design a new algorithm called SUBDTW to estimate when a trend appears and vanishes in a given time series. Based on the beginning and ending times of the trends, their temporal relationships can be modeled as a state machine representing the trend sequence. Since a scientific data set usually contains millions of data points, we propose an algorithm to extract important trend relationships in linear time complexity. We design novel user interfaces to explore the trend relationships, to visualize their temporal characteristics, and to display their spatial distributions. We use several scientific data sets to test our algorithm and demonstrate its utilities.