Daniel Orban

dblp:231/6649 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visualization and visual analytics · 67% Geometric modeling and processing · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Geometric modeling and processing
direct manipulation
0.412019
Drag and Track: A Direct Manipulation Interface for Contextualizing Data Instances within a Continuous Parameter Space · IEEE Trans. Vis. Comput. Graph. 2019
Visualization and visual analytics
ensemble visualization
0.412019
Drag and Track: A Direct Manipulation Interface for Contextualizing Data Instances within a Continuous Parameter Space · IEEE Trans. Vis. Comput. Graph. 2019
Visualization and visual analytics › high-dimensional data visualization
parameter space exploration
0.412019
Drag and Track: A Direct Manipulation Interface for Contextualizing Data Instances within a Continuous Parameter Space · IEEE Trans. Vis. Comput. Graph. 2019

Methods — techniques the papers use, named apart from their topics

direct manipulation · 0.4dimensionality reduction · 0.4
YearPublicationVenuePosition
2019 Drag and Track: A Direct Manipulation Interface for Contextualizing Data Instances within a Continuous Parameter Space
abstract
We present a direct manipulation technique that allows material scientists to interactively highlight relevant parameterized simulation instances located in dimensionally reduced spaces, enabling a user-defined understanding of a continuous parameter space. Our goals are two-fold: first, to build a user-directed intuition of dimensionally reduced data, and second, to provide a mechanism for creatively exploring parameter relationships in parameterized simulation sets, called ensembles. We start by visualizing ensemble data instances in dimensionally reduced scatter plots. To understand these abstract views, we employ user-defined virtual data instances that, through direct manipulation, search an ensemble for similar instances. Users can create multiple of these direct manipulation queries to visually annotate the spaces with sets of highlighted ensemble data instances. User-defined goals are therefore translated into custom illustrations that are projected onto the dimensionally reduced spaces. Combined forward and inverse searches of the parameter space follow naturally allowing for continuous parameter space prediction and visual query comparison in the context of an ensemble. The potential for this visualization technique is confirmed via expert user feedback for a shock physics application and synthetic model analysis.
Daniel Orban, Daniel F. Keefe, Ayan Biswas 0001, James P. Ahrens, David H. Rogers 0001
IEEE Trans. Vis. Comput. Graph.1