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Visualizing events in time-varying scientific data

  • Li LiuEmail author
  • Deborah Silver
  • Karen Bemis
Regular Paper
  • 13 Downloads

Abstract

In time-varying scientific datasets from simulations or experimental observations, scientists always need to understand when and where interesting events occur. An event is a complex spatial and temporal pattern that happens over a course of timesteps and includes the involved features and interactions. Event detection allows scientists to query a time-varying dataset from a much smaller set of possible choices. However, with many events detected from a dataset, each spanning different time intervals, querying and visualizing these events pose a challenge. In this work, we propose a framework for the visualization of events in time-varying scientific datasets. Our method extracts features from a data, tracks features over time, and saves the evolution process of features in an event database where a set of database operations are provided to model an event by defining the stages or individual steps that make up an event. Using the feature metadata and the event database, three types of event visualizations can be created to give a unique insight into the dynamics of data from temporal, spatial, and physical perspectives and to summarize multiple events or even the whole dataset. Three case studies are used to demonstrate the usability and effectiveness of the proposed approach.

Graphic abstract

Keywords

Scientific visualization Time-varying data Feature tracking Event detection Illustrative visualization 

Notes

Acknowledgements

We thank the Department of Environmental Sciences and the Department of Marine and Coastal Sciences at Rutgers University for providing the ocean simulation data and the underwater plume data. We also thank the domain experts who gave us feedback: Guangyu Xu, Darrell Jackson, Russ Light, Dujuan Kang, and Enrique Curchitser. This work has been funded by the US National Science Foundation (NSF) Grants 0825088 and OCE-1049088.

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Copyright information

© The Visualization Society of Japan 2020

Authors and Affiliations

  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Department of Electrical and Computer Engineering, RutgersThe State University of New JerseyNew BrunswickUSA
  3. 3.Department of Marine and Coastal Sciences, RutgersThe State University of New JerseyNew BrunswickUSA

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