Data provenance has led to a developing need for the technologies to empower end-users to assess and take action on the data life cycle. In the Big Data era, companies' amount of data over the world increases each day. As data increases, metadata on the data origin and lifecycle of data also overgrows. Thus, this requires innovations that can provide a better understanding and interpretation of data using data provenance. This study addresses the challenge of extracting data in the form of graphs from scientific workflows and facilitating demanded visualization approaches such as graph comparison, summarization, backward-forward querying, and stream data visualization. W3C-PROV-O provenance specification is implemented via a visualization tool to assess the applicability of proposed algorithms. The proposed algorithms are tested on a large-scale provenance dataset to explore their performance. In addition, this study discusses the details of a comprehensive usability study of the prototype visualization tool. Results indicate that proposed visualization approaches are usable and processing overhead is insignificant.

中文翻译：

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一种新颖的数据来源可视化方法

数据来源导致对技术的发展需求，以使最终用户能够评估数据生命周期并采取行动。在大数据时代，全球企业的数据量每天都在增加。随着数据的增加，关于数据来源和数据生命周期的元数据也会过度增长。因此，这需要能够使用数据来源更好地理解和解释数据的创新。本研究解决了从科学工作流程中以图形形式提取数据的挑战，并促进了所需的可视化方法，例如图形比较、摘要、后向查询和流数据可视化。W3C-PROV-O 出处规范是通过可视化工具实现的，以评估所提出算法的适用性。所提出的算法在大规模出处数据集上进行了测试，以探索它们的性能。此外，本研究讨论了原型可视化工具的全面可用性研究的细节。结果表明，所提出的可视化方法是可用的，并且处理开销微不足道。