Exploring the Possibilities of Embedding Heterogeneous Data Attributes in Familiar Visualizations

Area of Science:

• Computer Science
• Information Visualization
• Human-Computer Interaction

Background:

• Heterogeneous multi-dimensional data are increasingly ubiquitous.
• Existing visualization methods often propose novel, unfamiliar techniques.
• There is a need for enhancing familiar visualizations to handle complex data.

Purpose of the Study:

• To explore extending familiar visualizations with Heterogeneous Embedded Data Attributes (HEDA).
• To demonstrate HEDA as a generic, interactive component for enhancing visualization power.
• To investigate HEDA's application in familiar visualization layouts.

Main Methods:

• HEDA is presented as a tabular visualization building block.
• The design space of HEDA is explored through applications in the D3 gallery.
• Familiar visualizations are characterized by HEDA's data query capabilities.

Main Results:

• HEDA can extend common visualization techniques while preserving familiar layouts.
• HEDA enables visual observation, exploration, and querying of multivariate data within familiar visualizations.
• Attribute reordering within HEDA facilitates data queries.

Conclusions:

• HEDA offers a powerful method to enhance familiar visualizations for heterogeneous data.
• This approach makes complex data exploration more accessible by leveraging existing visualization knowledge.
• HEDA provides a flexible component for interactive data analysis.