Abstract
Data science applications tend to be built by composing tasks: discrete manipulations of data. These tasks are arranged in directed acyclic graphs, and many frameworks exist within the data science community supporting such a structure, which is called a workflow. In realistic applications, we want to be able to both analyze a workflow in the absence of data, and to execute the workflow with data. This paper combines effect handlers with arrow-like structures to abstract out data science tasks. This combination of techniques enables a modular design of workflows. Additionally, these workflows can both be analyzed prior to running (e.g., to provide early failure) and run conveniently. Our work is directly motivated by real-world scenarios, and we believe that our approach is applicable to new data science and machine learning applications and frameworks.
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