Virtual Reality & Intelligent Hardware (Aug 2022)
A Balanced-Partitioning Treemapping Method for Digital Hierarchical Dataset
Abstract
Background: The problem of visualizing a hierarchical dataset is an important and useful technical in many real happened situations. Folder system, stock market, and other hierarchical related dataset can use this technical for better understanding the structure, dynamic variation of the dataset. Traditional space-filling(square) based methods have advantages of compact space usage, node size showing compared to diagram based methods. While space-filling based methods have two main research directions—static and dynamic performance. We present a treemapping method based on balanced partitioning that enables in one variant very good aspect ratios, in another good temporal coherence for dynamic data and in the third a good compromise between these two aspects. To layout a treemap, we divide all children of a node into two groups. These groups are further divided until we reach groups of single elements. Then these groups are combined to form the rectangle representing the parent node. This process is performed for each layer of a given hierarchical dataset. In one variant of our partitioning we sort child elements first and built two as equal as possible sized groups from big and small elements(size-balanced partition), which achieves good aspect ratios for the rectangles, but less good temporal coherence(dynamic). The second variant takes the sequence of children and creates the as equal as possible groups with-out sorting(sequence-based, good compromise between aspect ratio and temporal coherency). The third variant splits the children sets always into two groups of equal cardinality regardless of their size(number-balanced, worse aspect ratios but good temporal coherence). We evaluate aspect ratios and dynamic stability of our methods and propose a new metric that measures the visual difference between rectangles during their movement for representing temporally changing inputs. We demonstrate that our treemapping via balanced partitioning out performs state-of-the-art methods for a number of real-world datasets.
