@inproceedings{10714266,

title = {Query-to- Vis: Conceptualization of a Broad-coverage Automated Visualization Pipeline},

author = {Uliana Eliseeva and Simon Heiß and Kawa Nazemi},

doi = {10.1109/IV64223.2024.00038},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {2024 28th International Conference Information Visualisation (IV)},

pages = {174-179},

abstract = {Automated visualization generation tools make visualization authoring more accessible to non-programmers and accelerate expert visualization designers in their work. Yet, combining these functions in one system remains a challenge for the research community because it has to maintain a high level of expressiveness and facilitate several tasks for diverse background users while remaining simple and intuitive. Providing a system that handles multiple tasks for heterogeneous user groups could be achieved, on the one hand, through unrestricted user input in the form of natural language. On the other hand, introducing a certain level of abstraction on multiple levels of the system can help integrate more tasks than implementing them separately. In this work, we present the concept of such a system with an LLM-based user query handling and mapping of the extracted components of the user task onto visual design choices with the help of deep learning. Our trained neural network shows promising results, suggesting the generalizability of the proposed approach.},

keywords = {Deep learning;Visualization;Pipelines;Neural networks;Natural languages;Diversity reception;automated visualization;neural networks;user query;LLM},

pubstate = {published},

tppubtype = {inproceedings}

}