Datasets of Visualization for Machine Learning
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914882702540800 |
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| author | Liu, Can Jiang, Ruike Tan, Shaocong Yu, Jiacheng Yang, Chaofan Shao, Hanning Yuan, Xiaoru |
| author_facet | Liu, Can Jiang, Ruike Tan, Shaocong Yu, Jiacheng Yang, Chaofan Shao, Hanning Yuan, Xiaoru |
| contents | Datasets of visualization play a crucial role in automating data-driven visualization pipelines, serving as the foundation for supervised model training and algorithm benchmarking. In this paper, we survey the literature on visualization datasets and provide a comprehensive overview of existing visualization datasets, including their data types, formats, supported tasks, and openness. We propose a what-why-how model for visualization datasets, considering the content of the dataset (what), the supported tasks (why), and the dataset construction process (how). This model provides a clear understanding of the diversity and complexity of visualization datasets. Additionally, we highlight the challenges faced by existing visualization datasets, including the lack of standardization in data types and formats and the limited availability of large-scale datasets. To address these challenges, we suggest future research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_16351 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Datasets of Visualization for Machine Learning Liu, Can Jiang, Ruike Tan, Shaocong Yu, Jiacheng Yang, Chaofan Shao, Hanning Yuan, Xiaoru Human-Computer Interaction Datasets of visualization play a crucial role in automating data-driven visualization pipelines, serving as the foundation for supervised model training and algorithm benchmarking. In this paper, we survey the literature on visualization datasets and provide a comprehensive overview of existing visualization datasets, including their data types, formats, supported tasks, and openness. We propose a what-why-how model for visualization datasets, considering the content of the dataset (what), the supported tasks (why), and the dataset construction process (how). This model provides a clear understanding of the diversity and complexity of visualization datasets. Additionally, we highlight the challenges faced by existing visualization datasets, including the lack of standardization in data types and formats and the limited availability of large-scale datasets. To address these challenges, we suggest future research directions. |
| title | Datasets of Visualization for Machine Learning |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2407.16351 |