Exploration of structured data using AI-enabled magic lenses

Abstract

In the era of increasingly complex and high-dimensional datasets, enabling intuitive and effective data exploration remains a significant challenge. This thesis presents a novel AI-enabled magic lens system designed to support rapid and insightful exploration of structured data through natural language interaction and dynamic visualization. The system projects the provided data onto a 2D canvas based on the prior dimensionality reduction. It leverages large language models (LLMs) to interpret user queries and generate contextually appropriate visualizations or textual responses. Beyond visual exploration, the system supports iterative dialogue with the AI, allowing users to refine visualizations and request explanations. Furthermore, users can document their findings through a notes panel. These notes can be evaluated using an LLM to provide feedback, contextual insights, and suggestions for further exploration. Additional key features include the ability to annotate clusters using LLM-generated labels, compare data subsets using multiple adjustable lenses, and view global visualizations for comparative analysis. To evaluate the system, an expert evaluation was conducted. Results indicate strong support for the system’s effectiveness and usability, highlighting the potential of LLM-enhanced data exploration tools. This work proves that the concept of integrating natural language interfaces and generative AI into interactive visualization systems can significantly enhance data analysis workflows, paving the way for future advancements in human-AI collaborative data exploration.