Porting a scientific data analysis workflow (DAW) to a cluster infrastructure, a new software stack, or even only a new dataset with some notably different properties is often challenging. Despite the structured definition of the steps (tasks) and their interdependencies during a complex data analysis in the DAW specification, relevant assumptions may remain unspecified and implicit. Such hidden assumptions often lead to crashing tasks without a reasonable error message, poor performance in general, non-terminating executions, or silent wrong results of the DAW, to name only a few possible consequences. Searching for the causes of such errors and drawbacks in a distributed compute cluster managed by a complex infrastructure stack, where DAWs for large datasets typically are executed, can be tedious and time-consuming.

中文翻译：

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数据分析工作流程的有效性约束

将科学数据分析工作流程 (DAW) 移植到集群基础设施、新的软件堆栈，甚至只是具有一些明显不同属性的新数据集通常具有挑战性。尽管 DAW 规范中的复杂数据分析过程中步骤（任务）及其相互依赖性的结构化定义，相关假设可能仍然未指定和隐含。这种隐藏的假设通常会导致任务崩溃而没有合理的错误消息、总体性能不佳、执行不终止或 DAW 的静默错误结果，仅举几个可能的后果。在由复杂基础架构堆栈管理的分布式计算集群（通常执行大型数据集的 DAW）中寻找此类错误和缺陷的原因可能是乏味且耗时的。