IEEE VIS 2024 Content: HiRegEx: Interactive Visual Query and Exploration of Multivariate Hierarchical Data

HiRegEx: Interactive Visual Query and Exploration of Multivariate Hierarchical Data

Guozheng Li - Beijing Institute of Technology, Beijing, China

haotian mi - Beijing Institute of Technology, Beijing, China

Chi Harold Liu - Beijing Institute of Technology, Beijing, China

Takayuki Itoh - Ochanomizu University, Tokyo, Japan

Guoren Wang - Beijing Institute of Technology, Beijing, China

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Room: Bayshore VII

2024-10-18T13:30:00ZGMT-0600Change your timezone on the schedule page
2024-10-18T13:30:00Z
Exemplar figure, described by caption below
The exploratory framework for querying multivariate hierarchical data comprises three modes: top-down, bottom-up, and context-creation. The top-down mode starts from a clear query task. Users construct the corresponding query expression through direct manipulations interactively. The bottom-up mode recommends related query expressions based on the initial expression and the multivariate hierarchical data collection. The context-creation mode offers users an overview of the entire hierarchical data collection. Modules associated with the top-down, bottom-up, and context creation modes in the framework are denoted by red, orange, and blue triangles.
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Keywords

Multivariate hierarchical data, declarative grammar, visual query

Abstract

When using exploratory visual analysis to examine multivariate hierarchical data, users often need to query data to narrow down the scope of analysis. However, formulating effective query expressions remains a challenge for multivariate hierarchical data, particularly when datasets become very large. To address this issue, we develop a declarative grammar,HiRegEx (Hierarchical data Regular Expression), for querying and exploring multivariate hierarchical data. Rooted in the extended multi-level task topology framework for tree visualizations (e-MLTT), HiRegEx delineates three query targets (node, path, and subtree) and two aspects for querying these targets (features and positions), and uses operators developed based on classical regular expressions for query construction. Based on the HiRegEx grammar, we develop an exploratory framework for querying and exploring multivariate hierarchical data and integrate it into the TreeQueryER prototype system. The exploratory framework includes three major components: top-down pattern specification, bottom-up data-driven inquiry, and context-creation data overview. We validate the expressiveness of HiRegEx with the tasks from the e-MLTT framework and showcase the utility and effectiveness ofTreeQueryER system through a case study involving expert users in the analysis of a citation tree dataset.