Why-Diff: Exploiting Provenance to Understand Outcome Differences From Non-Identical Reproduced Workflows
Why-Diff: Exploiting Provenance to Understand Outcome Differences From Non-Identical Reproduced Workflows
复制标题
Why-Diff：利用来源来了解不同复制工作流程的结果差异
作者：
Priyaa Thavasimani;J. Cala;P. Missier
诸如科学工作流之类的数据分析流程往往会被反复执行，其依赖关系和输入数据集各不相同。过去就有人提出要通过工作流步骤追踪最终信息产品的溯源，以实现其可重复性。在本文中，我们探讨这样一个假设：执行过程中记录的溯源轨迹对于回答从相似但不完全相同的工作流配置所获得的结果集之间观察到的差异相关问题也很有帮助。配置上的这种差异可能是有意引入的，即探索流程变化，也可能是意外产生的，通常是移植工作或计算环境变化的结果。以一种常用的工作流编程模型为参考，我们既考虑工作流的结构变化，也考虑其各个组件内部的变化。我们的“为何不同”算法比较从两个工作流变体派生的两个溯源轨迹的图表示。它生成一个差异图，可用于对工作流差异对观察到的输出差异的影响给出人类可读的解释。我们使用Neo4j图数据库进行报告。此外，我们使用一组合成工作流以及实际工作流报告工作流结果之间差异的解释。
Data analytics processes such as scientific workflows tend to be executed repeatedly, with varying dependencies and input datasets. The case has been made in the past for tracking the provenance of the final information products through the workflow steps, to enable their reproducibility. In this paper, we explore the hypothesis that provenance traces recorded during execution are also instrumental to answering questions about the observed differences between sets of results obtained from similar but not identical workflow configurations. Such differences in configurations may be introduced deliberately, i.e., to explore process variations or accidentally, typically as the result of porting efforts or of changes in the computing environment. Using a commonly used workflow programming model as a reference, we consider both structural variations in the workflows as well as variations within their individual components. Our why-diff algorithm compares the graph representations of two provenance traces derived from two workflow variations. It produces a delta graph that can be used to produce human-readable explanations of the impact of workflow differences on observed output differences. We report with Neo4j graph database. Further, we report explanations of the difference between workflow results using a suite of synthetic workflows as well as real-world workflows.
Sciunits：可重复使用的研究对象
DOI：
10.1109/escience.2017.51
发表时间：
2017
期刊：
IEEE 13th International Conference on e-Science (e-Science
影响因子：
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作者：
Ton That, Dai Hai;Fils, Gabriel;Yuan, Zhihao;Malik, Tanu
通讯作者：
Malik, Tanu