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Storing, reasoning, and querying OPM-compliant scientific workflow provenance using relational databases

机译:使用关系数据库存储,推理和查询符合OPM的科学工作流程来源

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Provenance, the metadata that records the derivation history of scientific results, is essential in scientific workflows to support the reproducibility of scientific discovery, result interpretation, and problem diagnosis. To promote and facilitate interoperability among heterogeneous provenance systems, the Open Provenance Model (0PM) was first proposed in 2008 and since then has played an important role in the community. In this paper, we present OPMProv, a relational database-based scientific workflow provenance system, that is compliant with 0PM (vl.l). Our main contributions are: (i) we design an entity-relationship diagram for 0PM and translate it into a relational database schema for the storage of provenance; (ii) we show that provenance reasoning defined in 0PM (vl.l) can be sufficiently supported by OPMProv using recursive views and SQL queries alone without any additional reasoning engine. Experiments are conducted to evaluate the performance of OPMProv in data insertion and provenance querying. A case study is performed, demonstrating that OPMProv can answer all except two queries out of the 16 queries defined in the Third Provenance Challenge.
机译:来源,记录科学结果的衍生历史的元数据,对于科学工作流程中的必不可少的内容,以支持科学发现,结果解释和问题诊断的可重复性。为了促进和促进异构源系统之间的互操作性,开放源模型(0PM)于2008年首次提出,此后在社区中发挥了重要作用。在本文中,我们介绍了OPMProv,这是一个基于关系数据库的科学工作流程出处系统,符合0PM(vl.l)。我们的主要贡献是:(i)我们为0PM设计了一个实体关系图,并将其转换为用于存储来源的关系数据库模式; (ii)我们证明了OPMProv仅使用递归视图和SQL查询就可以充分支持0PM(v.l)中定义的出处推理,而无需任何其他推理引擎。进行实验以评估OPMProv在数据插入和出处查询中的性能。进行了案例研究,表明OPMProv可以回答“第三种来源挑战”中定义的16个查询中的除两个查询之外的所有查询。

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