H
H. van de Wetering
Researcher at Eindhoven University of Technology
Publications - 11
Citations - 706
H. van de Wetering is an academic researcher from Eindhoven University of Technology. The author has contributed to research in topics: Visualization & Data visualization. The author has an hindex of 8, co-authored 11 publications receiving 675 citations.
Papers
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Proceedings ArticleDOI
Cushion treemaps: visualization of hierarchical information
J.J. van Wijk,H. van de Wetering +1 more
TL;DR: A new method is presented for the visualization of hierarchical information, such as directory structures and organization structures, based on recursive subdivision of a rectangular image space, which consists of recursive cushions.
Journal ArticleDOI
Composite Density Maps for Multivariate Trajectories
Roeland Scheepens,Niels Willems,H. van de Wetering,Gennady Andrienko,Natalia Andrienko,J.J. van Wijk +5 more
TL;DR: This paper presents a flexible architecture for density maps to enable custom, versatile exploration using multiple density fields and defines six different types of blocks to create, compose, and enhance trajectories or density fields.
Proceedings ArticleDOI
Botanical visualization of huge hierarchies
TL;DR: The strand model of Holton is used to convert an abstract tree into a geometric model and the elements, directories and files, as well as their relations can easily be extracted, thereby showing that the use of methods from botanical modeling can be effective for information visualization.
Journal ArticleDOI
Interactive visualization of state transition systems
TL;DR: A new method for the visualization of state transition systems is presented, where visual information is reduced by clustering nodes, forming a tree structure of related clusters that enables the user to relate features in the visualize of the state transition graph to semantic concepts in the corresponding process and vice versa.
Proceedings ArticleDOI
Visualization of state transition graphs
TL;DR: A new method for the visualization of state transition graphs is presented that reduces visual information by clustering nodes, forming a tree structure of related clusters that makes it easier to relate features in the visualize of the state transition graph to semantic concepts in the corresponding process.