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Provenance Collection Support in the Kepler Scientific Workflow System

机译:开普勒科学工作流程系统中的种源收集支持

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In many data-driven applications, analysis needs to be performed on scientific information obtained from several sources and generated by computations on distributed resources. Systematic analysis of this scientific information unleashes a growing need for automated data-driven applications that also can keep track of the provenance of the data and processes with little user interaction and overhead. Such data analysis can be facilitated by the recent advancements in scientific workflow systems. A major profit when using scientific workflow systems is the ability to make provenance collection a part of the workflow. Specifically, provenance should include not only the standard data lineage information but also information about the context in which the workflow was used, execution that processed the data, and the evolution of the workflow design. In this paper we describe a complete framework for data and process provenance in the Kepler Scientific Workflow System. We outline the requirements and issues related to data and workflow provenance in a multi-disciplinary workflow system and introduce how generic provenance capture can be facilitated in Kepler's actor-oriented workflow environment. We also describe the usage of the stored provenance information for efficient rerun of scientific workflows.
机译:在许多数据驱动的应用程序中,需要对从多个来源获得并通过对分布式资源进行计算而生成的科学信息进行分析。对这些科学信息的系统分析引发了对自动化数据驱动应用程序的日益增长的需求,这些应用程序还可以在很少的用户交互和开销的情况下跟踪数据和流程的来源。科学工作流程系统的最新发展可促进此类数据分析。使用科学的工作流程系统时,一个主要的好处就是能够将物产收集作为工作流程的一部分。具体而言,出处不仅应包括标准数据沿袭信息,而且还应包括有关使用工作流的上下文,处理数据的执行情况以及工作流设计的演变的信息。在本文中,我们描述了开普勒科学工作流程系统中数据和过程来源的完整框架。我们概述了多学科工作流系统中与数据和工作流源有关的要求和问题,并介绍了如何在开普勒面向演员的工作流环境中促进通用源捕获。我们还将描述所存储的出处信息的用法,以有效地重新运行科学工作流程。

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