Abstract

Multidimensional hierarchical (mTree) data are very common in daily life and scientific research. However, mTree data exploration is a laborious and time-consuming process due to its structural complexity and large dimension combination space. To address this problem, we present mTreeIllustrator, a mixed-initiative framework for exploratory analysis of multidimensional hierarchical data with faceted visualizations. First, we propose a recommendation pipeline for the automatic selection and visual representation of important subspaces of mTree data. Furthermore, we design a visual framework and an interaction schema to couple automatic recommendations with human specifications to facilitate progressive exploratory analysis. Comparative experiments and user studies demonstrate the usability and effectiveness of our framework.

Keywords

Cite this article

@article{cai-6209,
  author  = {Guijuan Wang and Yu Zhao and Boyou Tan and Zhong Wang and Jiansong Wang and Hao Guo and Yadong Wu},
  title   = {mTreeIllustrator: A Mixed-Initiative Framework for Visual Exploratory Analysis of Multidimensional Hierarchical Data},
  journal = {Computing and Informatics},
  volume  = {42},
  number  = {3},
  pages   = {690--715},
  year    = {2023},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2023_3_690}
}
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