Hyperbolic Tree for Effective Visualization of Large Extensible Data Standards Research-in-Progress
Yinghua Ma, Hongwei Zhu, Guiyang Su, Tubagus Mohammad Akhriza · 2011
Large data standards specify tens of thousands of data elements that have intricate relationships. Without effective visualization, it is extremely difficult to understand such large data standards. This is further exacerbated when users are allowed to extend data standards, which in effect produces multiple versions of data standards. In this research, we develop a hyperbolic tree based visualization technique that uses different colors of node labels to distinguish different groups of relationships. Edge colors are also differentiated to visualize extensions to a given standard. The technique is applied to a real-world financial reporting data standard called the XBRL GAAP Taxonomy. Ongoing research will further enhance the technique and evaluate its effectiveness in helping users understand large data standards.