用可视化方法提升多类型数据的准备度，助力AI项目落地。
Exploratory Visual Analysis for Increasing Data Readiness in Artificial Intelligence Projects
- 构建数据准备度与可视化技术的映射关系
- 在时序数据中实现数值、分类、文本数据的准备度提升
- 关注任务与解决方案，应对数据分布变化
本文分享了利用可视化分析方法提升异构数据在人工智能项目中数据准备度的经验与教训。提升数据准备度需同时理解数据本身及其使用背景，这正是可视化分析的优势所在。为此，我们提出了一种针对不同数据类型的数据准备度要素与可视化技术的映射关系。基于该映射，我们在包含时变数值、分类和文本数据的应用场景中提升了数据准备度水平。此外，我们扩展了数据准备度概念，更充分考虑任务与解决方案因素，并明确处理数据收集期间的分布漂移问题。通过实际应用，我们报告了这些可视化技术在辅助未来人工智能项目提升数据准备度方面的有效性。
原文摘要 · Abstract (English)
We present experiences and lessons learned from increasing data readiness of heterogeneous data for artificial intelligence projects using visual analysis methods. Increasing the data readiness level involves understanding both the data as well as the context in which it is used, which are challenges well suitable to visual analysis. For this purpose, we contribute a mapping between data readiness aspects and visual analysis techniques suitable for different data types. We use the defined mapping to increase data readiness levels in use cases involving time-varying data, including numerical, categorical, and text. In addition to the mapping, we extend the data readiness concept to better take aspects of the task and solution into account and explicitly address distribution shifts during data collection time. We report on our experiences in using the presented visual analysis techniques to aid future artificial intelligence projects in raising the data readiness level.
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