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Visualization and Visual Analysis of Ensemble Data: A Survey
- Source :
- IEEE Transactions on Visualization and Computer Graphics. 25:2853-2872
- Publication Year :
- 2019
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2019.
-
Abstract
- Over the last decade, ensemble visualization has witnessed a significant development due to the wide availability of ensemble data, and the increasing visualization needs from a variety of disciplines. From the data analysis point of view, it can be observed that many ensemble visualization works focus on the same facet of ensemble data, use similar data aggregation or uncertainty modeling methods. However, the lack of reflections on those essential commonalities and a systematic overview of those works prevents visualization researchers from effectively identifying new or unsolved problems and planning for further developments. In this paper, we take a holistic perspective and provide a survey of ensemble visualization. Specifically, we study ensemble visualization works in the recent decade, and categorize them from two perspectives: (1) their proposed visualization techniques; and (2) their involved analytic tasks. For the first perspective, we focus on elaborating how conventional visualization techniques (e.g., surface, volume visualization techniques) have been adapted to ensemble data; for the second perspective, we emphasize how analytic tasks (e.g., comparison, clustering) have been performed differently for ensemble data. From the study of ensemble visualization literature, we have also identified several research trends, as well as some future research opportunities.
- Subjects :
- Creative visualization
business.industry
Computer science
media_common.quotation_subject
Perspective (graphical)
020207 software engineering
02 engineering and technology
Computer Graphics and Computer-Aided Design
Data science
Data modeling
Variety (cybernetics)
Visualization
Data visualization
Signal Processing
0202 electrical engineering, electronic engineering, information engineering
Computer Vision and Pattern Recognition
business
Cluster analysis
Software
media_common
Subjects
Details
- ISSN :
- 21609306 and 10772626
- Volume :
- 25
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Visualization and Computer Graphics
- Accession number :
- edsair.doi.dedup.....5773f4ece44f258bd55c1210e894de49