Visual analytics tools integrate provenance recording to externalize analytic processes or user insights. Provenance can be captured on varying levels of detail, and in turn activities can be characterized from different granularities. However, current approaches do not support inferring activities that can only be characterized across multiple levels of provenance. We propose a task abstraction framework that consists of a three stage approach, composed of 1) initializing a provenance task hierarchy, 2) parsing the provenance hierarchy by using an abstraction mapping mechanism, and 3) leveraging the task hierarchy in an analytical tool. Furthermore, we identify implications to accommodate iterative refinement, context, variability, and uncertainty during all stages of the framework. We describe a use case which exemplifies our abstraction framework, demonstrating how context can influence the provenance hierarchy to support analysis. The article concludes with an agenda, raising and discussing challenges that need to be considered for successfully implementing such a framework.

中文翻译：

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来源任务抽象框架。

视觉分析工具集成了出处记录，以外部化分析过程或用户见解。可以在不同的细节级别上捕获来源，然后可以通过不同的粒度来表征活动。但是，当前的方法不支持只能在多个来源级别进行表征的推断活动。我们提出了一个任务抽象框架，该框架由三个阶段的方法组成，包括：1）初始化出处任务层次结构； 2）通过使用抽象映射机制解析出处层次结构； 3）在分析工具中利用任务层次结构。此外，我们确定了在框架所有阶段适应迭代细化，上下文，可变性和不确定性的含义。我们描述了一个用例，该用例例证了我们的抽象框架，演示了上下文如何影响源层次以支持分析。本文以议程结尾，提出并讨论了成功实施此类框架需要考虑的挑战。