Work

A survey of visual insight mining: Connecting data and insights via visualization

Year: 2025

Type: article

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 (i.e., 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.

Source: Visual Informatics

DOI: 10.1016/j.visinf.2025.100271

Authors: Yijie Lian, Jianing Hao, Wei Zeng, Qiong Luo

Institutions: The Hong Kong University of Science and Technology (Guangzhou), Hong Kong University of Science and Technology

Language: English

Topic: Data Visualization and Analytics

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Natural Science Foundation of China, Hong Kong University of Science and Technology

Award: G4023314675