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.

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来源任务抽象框架

可视化分析工具集成了出处记录，以将分析过程或用户洞察具体化。可以在不同的细节级别上捕获出处，进而可以从不同的粒度表征活动。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。我们提出了一个由三阶段方法组成的任务抽象框架，包括 1) 初始化来源任务层次结构，2) 通过使用抽象映射机制解析来源层次结构，以及 3) 在分析工具中利用任务层次结构。此外，我们确定了在框架的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，它举例说明了我们的抽象框架，展示了上下文如何影响来源层次结构以支持分析。文章最后提出了一个议程，提出并讨论了成功实施这样一个框架需要考虑的挑战。