Data quality management and assessment play a vital role for ensuring the trust in the data and its fitness-of-use for subsequent analysis. The transformation history of a data wrangling system is often insufficient for determining the usability of a dataset, lacking information how changes affected the dataset. Capturing workflow provenance along the wrangling process and combining it with descriptive information as data provenance can enable users to comprehend how these changes affected the dataset, and if they benefited data quality. We present DQProv Explorer, a system that captures and visualizes provenance from data wrangling operations. It features three visualization components: allowing the user to explore the provenance graph of operations and the data stream, the development of quality over time for a sequence of wrangling operations applied to the dataset, and the distribution of issues across the entirety of the dataset to determine error patterns.

中文翻译：

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从数据整理中捕获和可视化出处

数据质量管理和评估对于确保对数据的信任及其对后续分析的适用性起着至关重要的作用。数据整理系统的转换历史通常不足以确定数据集的可用性，缺乏变化如何影响数据集的信息。在争论过程中捕获工作流来源并将其与作为数据来源的描述性信息相结合，可以使用户了解这些变化如何影响数据集，以及它们是否对数据质量有益。我们展示了 DQProv Explorer，这是一个从数据整理操作中捕获和可视化来源的系统。它具有三个可视化组件：允许用户探索操作和数据流的来源图，