FeatureFlow: exploring feature evolution for time-varying volume data
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Time-varying volume data generated from scientific simulations are generally temporal and contain dynamic and complex features. The evolution of these features is important to understand the phenomena hidden in the data. In this paper, we introduce FeatureFlow, which is a novel visualization technique revealing feature evolution based on a hierarchical river metaphor. FeatureFlow decomposes the entire feature evolution into multiple levels and exploits an evolution measure to quantify the changes of the features. FeatureFlow visually summarizes the hierarchical evolution, the evolution value, and associated attributes to intuitively display the complex 4D spatial-temporal feature evolution in 2D. In addition, FeatureFlow converts each river into a string based on the serial ordering of evolutionary events and supports evolutionary pattern-matching queries. Experiments on three time-varying volume data sets and feedback from two domain experts demonstrate the utility of FeatureFlow in effectively helping users understand and explore feature evolution in time-varying volume data.
KeywordsTime-varying volume data Feature evolution Hierarchical river metaphor Pattern-matching query
The authors would like to thank the anonymous reviewers for their valuable comments. This work was supported by the National Key Research & Development Program of China (2017YFB0202203), National Natural Science Foundation of China (61672452 and 61890954), and NSFC-Guangdong Joint Fund (U1611263).
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