Exploring multidimensional hierarchical data is a long-standing problem present in a wide range of fields such as bioinformatics, software systems, social science and business intelligence. While each hierarchical dimension within these data structures can be explored in isolation, critical information lies in the relationships between these dimensions. Although existing approaches such as parallel sets can simultaneously visualize multiple non-hierarchical dimensions, and others such as tree-map or honeycomb can visualize one or two hierarchical dimensions, the challenge of visualizing multi-dimensional hierarchical data remains open.
To address this problem, we have developed a novel data visualization approach – Parallel Hierarchies – which we demonstrate on a real-life product called SAP Product Lifecycle Costing started at SAP SE. The starting point of the research was a thorough customer driven requirement engineering phase including iterative design principles.
To demonstrate the generality of the approach, Parallel Hierarchies were applied to datasets in bioinformatics and social sciences. Finally, several extensions that enable interactive version comparison as well as the visualization of data uncertainty are designed on the basis of Flow diagrams.