Hierarchies, also know as tree structures, are collections of data nodes where each node has a unique parent (node above it in the hierarchy), but may have many siblings (nodes below it in the hierarchy). In general, the nodes and the links between them can have multiple attributes. The entire structure of the hierarchy and its encompassing relations is also usually relevant. Tasks can be applied to a single node, a link, a collection of nodes, or even to the entire structure.

Hierarchical data is very diverse and is encountered in many forms. Hierarchies naturally arise in taxonomies, the structures of organizations, disk space management, genealogies, and the Dewey Decimal system. Uses of hierarchies are almost as vast, including: finding a particular node, viewing a node in the context of the entire hierarchy, examining the over-all structure and relations of the tree, and even finding duplicates or anomalies within the tree structure.

The traditional presentation of hierarchies usually consist of a 2-D representation where child nodes are positioned under their parents in wedge-like formations. Representing trees in this manner severly limits both the depth and breadth of the tree that one can view at a single time. Furthermore, navigating and finding specific nodes in such a structure can be confusing, disorienting and downright frustrating. More recent visualization techniques attempt to show many more nodes, if not the entire tree itself, as well as providing mechanisms for navigation and searching which allows the user to keep the context of the entire tree in mind as well as reducing the disorientation.

Hierarchies can be seen as a special case of networks, except the definition of hierarchies eliminates the possibilities of dual paths and cycles. Every node in a hierarchy also has a unique path to the root node which is not guaranteed to be the case with networks. Visualization of hierarchies is related to 3-D data, as many visualizations use 3-D graphics to render the results. Hierarchies are similiar to the multi-dimensional data since the nodes in hierarchies usually contain a fair number of attributes, but multi-dimensional data does not contain the intrisic hierarchy in the data that hierarchical data usually provides (e.g. folders and files on a disk).

The major players in the hierarchical visualization are Xerox PARC, with their Cone Trees, and Hyperbolic Trees approachs, both of which have been copied or used as a basis for many other projects. Treemaps, is another significant representation in this genre, which was developed in the HCIL at the University of Maryland.






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