ProvONE+: A Provenance Model for Scientific Workflows
ProvONE+: A Provenance Model for Scientific Workflows
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ProvONE：科学工作流程的起源模型
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
P. Fitch
中科院分区：
文献类型：
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作者：
Anila Sahar Butt;P. Fitch
工作流的来源对于它们派生的数据和它们的规范都是至关重要的，以便在科学界中实现信息的可再现性、共享和重用。虽然科学工作流起源的形式化建模在语义网等许多领域受到关注和研究，但我们意识到，目前还没有建立控制流驱动的科学工作流起源模型。语义web社区为数据驱动的科学工作流提出的溯源模型可以捕获控制流驱动的工作流执行轨迹的溯源（即回溯溯源），但没有详细说明工作流结构（即工作流溯源）。工作流程的不明确或不完整的结构会导致对科学实验的误解，并妨碍对工作流程的一致性检查，从而限制了来源的收益。为了克服这一限制，我们为涉及科学工作流的控制流提出了一个正式的、轻量级的和通用的规范模型。该模型可以与现有的来源模型相结合，并且易于扩展以指定通用的控制流模式。在本文中，我们激发了对控制流驱动的科学工作流来源模型的需求，提供了其关键类和属性的概述，并简要讨论了它与ProvONE来源模型的集成以及它与ProvONE来源模型的兼容性。我们还将重点关注使用所提出的模型的样本建模，并从农业领域提出一个全面的实施场景来验证模型。
The provenance of workflows is essential, both for the data they derive and for their specification, to allow for the reproducibility, sharing and reuse of information in the scientific community. Although the formal modelling of scientific workflow provenance was of interest and studied, in many fields like semantic web, yet no provenance model has existed, we are aware of, to model control-flow driven scientific workflows. The provenance models proposed by the semantic web community for data-driven scientific workflows may capture the provenance of control-flow driven workflows execution traces (i.e., retrospective provenance) but underspecify the workflow structure (i.e., workflow provenance). An underspecified or incomplete structure of a workflow results in the misinterpretation of a scientific experiment and precludes conformance checking of the workflow, thereby restricting the gains of provenance. To overcome the limitation, we present a formal, lightweight and general-purpose specification model for the control-flows involved scientific workflows. The proposed model can be combined with the existing provenance models and easy to extend to specify the common control-flow patterns. In this article, we inspire the need for control-flow driven scientific workflow provenance model, provide an overview of its key classes and properties, and briefly discuss its integration with the ProvONE provenance model as well as its compatibility to PROV-DM. We will also focus on the sample modelling using the proposed model and present a comprehensive implementation scenario from the agricultural domain for validating the model.