Paper Info
Title
Exploring the Possibilities of Embedding Heterogeneous Data Attributes in Familiar Visualizations.
Abstract
Heterogeneous multi-dimensional data are now sufficiently common that they can be referred to as ubiquitous. The most frequent approach to visualizing these data has been to propose new visualizations for representing these data. These new solutions are often inventive but tend to be unfamiliar. We take a different approach. We explore the possibility of extending well-known and familiar visualizations through including Heterogeneous Embedded Data Attributes (HEDA) in order to make familiar visualizations more powerful. We demonstrate how HEDA is a generic, interactive visualization component that can extend common visualization techniques while respecting the structure of the familiar layout. HEDA is a tabular visualization building block that enables individuals to visually observe, explore, and query their familiar visualizations through manipulation of embedded multivariate data. We describe the design space of HEDA by exploring its application to familiar visualizations in the D3 gallery. We characterize these familiar visualizations by the extent to which HEDA can facilitate data queries based on attribute reordering.
Year
DOI
Venue
2017
10.1109/TVCG.2016.2598586
IEEE Trans. Vis. Comput. Graph.
Keywords
Field
DocType
Data visualization,Layout,Visualization,Space exploration,Context,Joining processes,Encoding
Design space,Computer vision,Data visualization,Embedding,Information visualization,Visualization,Computer science,Theoretical computer science,Interactive visualization,Artificial intelligence,Creative visualization,Encoding (memory)
Journal
Volume
Issue
ISSN
23
1
1077-2626
Citations 
PageRank 
References 
10
0.51
34
Authors
4
Name
Order
Citations
PageRank
Mona Hosseinkhani Loorak1100.51
Charles Perin222816.52
Christopher Collins3103749.74
Sheelagh Carpendale44431251.97