Exploring the Possibilities of Embedding Heterogeneous Data Attributes in Familiar Visualizations

Mona Hosseinkhani Loorak, Charles Perin, Christopher Collins, Sheelagh Carpendale
2017 IEEE Transactions on Visualization and Computer Graphics  
This is the accepted version of the paper. This version of the publication may differ from the final published version. Permanent repository link: http://openaccess.city.ac.uk/16705/ Link to published version: http://dx.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
more » ... ut 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.
doi:10.1109/tvcg.2016.2598586 pmid:27875173 fatcat:5vjqm3ahabezteyswlnotukevm