科学的工作流重写,同时保留来源

Sarah Cohen Boulakia, C. Froidevaux, Jiuqiang Chen
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引用次数: 7

摘要

科学的工作流系统数量众多,并配备了能够收集工作流运行期间产生和消耗的数据的来源模块,以提高可重复性。已经开发了越来越多的方法来帮助管理来源信息。其中一些能够在多项式时间内处理数据,但它们要求工作流具有串并联(SP)结构。因此,将任何工作流重写为SP工作流尤为重要。在本文中,(i)我们引入了等价出处重写过程的概念,(ii)我们回顾了现有的图转换,(iii)我们设计了等价出处SPFlow算法,(iv)我们在一千多个实际工作流中评估了我们的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scientific workflow rewriting while preserving provenance
Scientific workflow systems are numerous and equipped of provenance modules able to collect data produced and consumed during workflow runs to enhance reproducibility. An increasing number of approaches have been developed to help managing provenance information. Some of them are able to process data in a polynomial time but they require workflows to have series-parallel (SP) structures. Rewriting any workflow into an SP workflow is thus particularly important. In this paper, (i) we introduce the concept of provenance-equivalent rewriting process, (ii) we review existing graph transformations, (iii) we design the provenance-equivalent SPFlow algorithm, (iv) we evaluate our approach over a thousand of real workflows.
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