研究脏数据如何影响AI绘图工具，找出常见问题并提出改进方向。
Formative Study for AI-assisted Data Visualization
- 通过有缺陷数据生成图表，识别具体可视化问题
- 发现当前工具难以应对数据错误，易产生误导性结果
- 适合数据科学家和工具开发者参考，提升系统鲁棒性
本研究通过在存在固有质量问题的数据集上生成可视化图表，探究数据质量对AI辅助数据可视化的影响。研究旨在识别并分类由未清洗数据引发的具体可视化问题，并探索高效解决这些挑战的方法与工具。尽管尚未开展工具开发工作，但研究强调需增强AI可视化工具对异常数据的处理能力。成果表明，亟需更强大、用户友好的解决方案，以实现对数据与可视化错误的快速修正，从而提升整个流程的可靠性与可用性。
原文摘要 · Abstract (English)
This formative study investigates the impact of data quality on AI-assisted data visualizations, focusing on how uncleaned datasets influence the outcomes of these tools. By generating visualizations from datasets with inherent quality issues, the research aims to identify and categorize the specific visualization problems that arise. The study further explores potential methods and tools to address these visualization challenges efficiently and effectively. Although tool development has not yet been undertaken, the findings emphasize enhancing AI visualization tools to handle flawed data better. This research underscores the critical need for more robust, user-friendly solutions that facilitate quicker and easier correction of data and visualization errors, thereby improving the overall reliability and usability of AI-assisted data visualization processes.
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