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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/223487
Bridging Natural Language and Hierarchical Multivariate Data Visualisation to Support Data Analysis
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[eng] Tracking and analysing the vast amounts of data generated from social networks and digital platforms presents important challenges, not only due to the overwhelming volume but also the complex relationships embedded within the data. This thesis addresses these challenges through data visualisation techniques, focusing on hierarchical and multivariate data, where visual clutter and effective use of space are key concerns. Furthermore, the rise of Visual Natural Language Interfaces (V-NLIs), also referred to in this thesis as VisChatbots, offers new opportunities to facilitate the interaction with data visualisations via natural language.
This thesis contributes to the fields of Hierarchical Multivariate Data Visualisation and Visualisation-oriented Natural Language Interfaces. Specifically, we introduce a novel categorization algorithm to classify hierarchical data, from which we propose the most suitable visual designs for their visualisation. Additionally, we propose a new incremental design methodology for Vis-Chatbots, called VisChat. This structured approach guides the development of chatbots integrated into visualisation platforms, establishing smooth communication among stakeholders—end users, designers, and developers—and introducing new design artefacts, such as the VisAgent persona, visualisation conversation patterns, and conversational transcripts that help guide and validate the design of the VisChatbot.
Following the VisChat methodology, we have integrated a VisChatbot into a platform for
visualising hierarchical and multivariate data. To validate our proposal, we present a case study on the analysis of hate speech in online news articles, where the suitability of the proposed visualisations was evaluated, as well as the capability of the visualisation chatbot to enable users to easily explore and understand, through Natural Language interactions, both the structural relationships and the feature-based relationships within the data.
In conclusion, this thesis not only advances data visualisation techniques for multivariate
hierarchical data but also establishes a framework for integrating natural language interfaces intov isual analysis platforms, thereby promoting a more efficient and effective analysis of data.
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KAVAZ, Ecem. Bridging Natural Language and Hierarchical Multivariate Data Visualisation to Support Data Analysis. [consulta: 30 de novembre de 2025]. [Disponible a: https://hdl.handle.net/2445/223487]