"HIERARCHY VIZ: A VISUAL ANALYTICS FRAMEWORK FOR VISUALIZING HIERARCHI" by Vinay Kumar Uppalapati

Date Approved

6-16-2025

Embargo Period

6-16-2026

Document Type

Thesis

Degree Name

M.S. Data Science

Department

Computer Science

College

College of Science & Mathematics

Advisor

Bo Sun, Ph.D.

Committee Member 1

Shen Shyang Ho, Ph.D.

Committee Member 2

Sihan Yu, Ph.D.

Disciplines

Computer Sciences | Physical Sciences and Mathematics

Abstract

Automated visualization systems aim to generate visualizations directly from raw data with minimal user inputs. However, while existing systems focus on data visualizations mainly using line charts and scatter plots to explore the data patterns, they struggle with hierarchical data representation where data relationship is essential. Hierarchical visualization, crucial for understanding multi-level relationships, typically requires users to manually define hierarchies and have expertise in visualization tools to create meaningful representations. This makes the process complex, time-consuming, and reliant on domain knowledge. To address this, we propose HierarchyViz, an automated system that detects multiple hierarchies in raw datasets and generates intuitive visualizations by default, while also allowing optional manual refinement for user-specific needs. The system utilizes Sentence Transformers to generate embeddings for column names, followed by hierarchical clustering to group semantically related attributes. A custom machine learning model predicts hierarchical relationships. HierarchyViz automatically visualizes these hierarchies using drill-up/drill-down bar charts and tree maps, while providing an interactive tree structure and a dynamic level pyramid visualization. By automating both hierarchy detection and visualization, HierarchyViz reduces the need for manual hierarchy definition and visualization expertise, making hierarchical data exploration more accessible and advancing automated data visualization.

Available for download on Tuesday, June 16, 2026

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