Machine Learning Methods in Visualisation for Big Data 2024

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https://diglib.eg.org/handle/10.2312/3606999

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Browsing Machine Learning Methods in Visualisation for Big Data 2024 by Subject "Human centered computing → Visualization design and evaluation methods"

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    Exploration of Preference Models using Visual Analytics
    (The Eurographics Association, 2024) Buchmüller, Raphael; Zymla, Mark-Matthias; Keim, Daniel; Butt, Miriam; Sevastjanova, Rita; Archambault, Daniel; Nabney, Ian; Peltonen, Jaakko
    The identification and integration of diverse viewpoints are key to sound decision-making. This paper introduces a novel Visual Analytics technique aimed at summarizing and comparing perspectives derived from established preference models. We use 2D projection and interactive visualization to explore user models based on subjective preference labels and extracted linguistic features. We then employ a pie-chart-like exploration design to enable the aggregation and simultaneous exploration of diverse preference groupings. The approach allows rotation and slicing interactions of the visual space. We demonstrate the technique's applicability and effectiveness through a use case in exploring the complex landscape of argument preferences. We highlight our designs potential to enhance decision-making processes within diverging preferences through Visual Analytics.
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    User-Adaptive Visualizations: An Exploration with GPT-4
    (The Eurographics Association, 2024) Yanez, Fernando; Nobre, Carolina; Archambault, Daniel; Nabney, Ian; Peltonen, Jaakko
    Data visualizations aim to enhance cognition and data interpretation. However, individual differences impact visual analysis, suggesting a personalized approach may be more effective. Current efforts focus on the study of generating visualizations with Large Language Models, lacking the user personalization component. This project explores using such models, specifically GPT-4, for modifying data visualizations to tailor to individual user characteristics. We developed a study to test GPT-4's ability to generate personalized visualizations. Statistical analysis of our results shows that for some personas, GPT is effective at personalizing the visualization. However, not all personalizations led to statistically significant improvements, suggesting variability in the effectiveness of LLM-driven personalization. These findings underline the importance of further exploring how personalized visualizations can best meet diverse user needs.