Mind-Mapping Data Analysis with LLMs: From Vision to First Steps

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dc.contributor.authorJianu, Raduen_US
dc.contributor.authorHutchinson, Maeveen_US
dc.contributor.authorAndrienko, Nataliaen_US
dc.contributor.authorAndrienko, Gennadyen_US
dc.contributor.authorElshehaly, Maien_US
dc.contributor.authorSlingsby, Aidanen_US
dc.contributor.editorSheng, Yunen_US
dc.contributor.editorSlingsby, Aidanen_US
dc.date.accessioned2025-09-09T05:12:51Z
dc.date.available2025-09-09T05:12:51Z
dc.date.issued2025
dc.description.abstractWe explore how large language models (LLMs) can support real-time visual mapping of data analysis workflows. Building on an earlier vision, we investigate if and how LLMs can decompose analytic dialogues into ''analysis maps'' that capture key semantic units such as questions, datasets, tasks, and findings. Using two exemplar analyses, we test both post-hoc and interactive strategies for generating these maps and experiment with prompting techniques for structuring and updating them. Results, documented in Observable notebooks, suggest that LLMs can scaffold analysis-as-network meaningfully-laying the groundwork for user-facing systems and moving beyond purely textual forms of LLM-mediated analysis.en_US
dc.description.sectionheadersShort Papers Session: Visualisation
dc.description.seriesinformationComputer Graphics and Visual Computing (CGVC)
dc.identifier.doi10.2312/cgvc.20251221
dc.identifier.isbn978-3-03868-293-6
dc.identifier.pages4 pages
dc.identifier.urihttps://doi.org/10.2312/cgvc.20251221
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/cgvc20251221
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Collision detection; Hardware → Sensors and actuators; PCB design and layout
dc.subjectComputing methodologies → Collision detection
dc.subjectHardware → Sensors and actuators
dc.subjectPCB design and layout
dc.titleMind-Mapping Data Analysis with LLMs: From Vision to First Stepsen_US
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