Exploring the Possibilities of Embedding Heterogeneous Data Attributes in Familiar Visualizations
Mona Hosseinkhani
Charles Perin
Christopher Collins
Sheelagh Carpendale
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Overview
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.
Supplemental materials are available here.
Publications
| Mona Hosseinkhani Loorak, Charles Perin, Christopher Collins and Sheelagh Carpendale. Exploring the Possibilities of Embedding Heterogeneous Data Attributes in Familiar Visualizations. IEEE Transactions on Visualization and Computer Graphics, 23(1):581-590, jan, 2017. | 
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