A Survey of Visual Insight Mining:Connecting Data and Insights via Visualization

The Hong Kong University of Science and Technology (Guangzhou)

Data Science and Analytics Thrust

PhD Qualifying Examination

By Mr LIAN, Yijie

Abstract

Insight mining transforms complex data into actionable knowledge, enabling effective decision-making across diverse domains. Given the richness and interpretative power of visualizations, visual insight mining - the process of extracting meaningful insights from raw data through intuitive visual representations - has become increasingly vital. This survey systematically reviews the current landscape of visual insight mining, addressing the critical questions: “How can visualizations be generated from data?” and “How can insights be extracted from visualizations?”. Specifically, we delve into six distinct tasks (, task decomposition, visualization generation, visualization recommendation, chart parsing, chart question answering, and insight generation) in the process of visual insight mining, and provide a comprehensive analysis of rule-based, learning-based, and large-model-based methods for each task. Based on the survey, we discuss current research challenges and outline future opportunities. By viewing visualization as a bridge in the data-to-insight path, this survey offers a structured foundation for further exploration in visual insight mining.

PQE Committee

Chair of Committee: Prof. TANG Nan

Prime Supervisor: Prof. LUO Qiong 

Co-Supervisor: Prof. ZENG Wei

Examiner: Prof. WEN Zeyi