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Jarry H. T. Claessen

dblp:63/10398 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2011
—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 · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics
multivariate data visualization
0.112011
Flexible Linked Axes for Multivariate Data Visualization · IEEE Trans. Vis. Comput. Graph. 2011
Visualization and visual analytics › multi-view visualization › coordinated multiple views
brushing and linking
0.012011
Flexible Linked Axes for Multivariate Data Visualization · IEEE Trans. Vis. Comput. Graph. 2011

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

scatterplot · 0.1parallel coordinates · 0.1
YearPublicationVenuePosition
2011 Flexible Linked Axes for Multivariate Data Visualization
abstract
Multivariate data visualization is a classic topic, for which many solutions have been proposed, each with its own strengths and weaknesses. In standard solutions the structure of the visualization is fixed, we explore how to give the user more freedom to define visualizations. Our new approach is based on the usage of Flexible Linked Axes: The user is enabled to define a visualization by drawing and linking axes on a canvas. Each axis has an associated attribute and range, which can be adapted. Links between pairs of axes are used to show data in either scatter plot- or Parallel Coordinates Plot-style. Flexible Linked Axes enable users to define a wide variety of different visualizations. These include standard methods, such as scatter plot matrices, radar charts, and PCPs [11]; less well known approaches, such as Hyperboxes [1], TimeWheels [17], and many-to-many relational parallel coordinate displays [14]; and also custom visualizations, consisting of combinations of scatter plots and PCPs. Furthermore, our method allows users to define composite visualizations that automatically support brushing and linking. We have discussed our approach with ten prospective users, who found the concept easy to understand and highly promising.
Jarry H. T. Claessen, Jarke J. van Wijk
IEEE Trans. Vis. Comput. Graph.1