A Provenance Task Abstraction Framework

A Provenance Task Abstraction Framework
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起源任务抽象框架

DOI:
10.1109/mcg.2019.2945720
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发表时间:
2019
影响因子:
1.8
通讯作者:
Laramee, Robert S.
Laramee, Robert S.
中科院分区:
计算机科学4区
文献类型:
--
作者:
Bors, Christian;Wenskovitch, John;Dowling, Michelle;Attfield, Simon;Battle, Leilani;Endert, Alex;Kulyk, Olga;Laramee, Robert S.

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可视化分析工具集成了出处记录,以具体化分析过程或用户见解。起源可以在不同的细节级别上被捕获,反过来,活动可以从不同的粒度来表征。然而,目前的方法不支持推断活动,只能在多个层次的起源的特点。我们提出了一个任务抽象框架,包括一个三阶段的方法,包括1)初始化的起源任务层次结构,2)通过使用抽象映射机制解析的起源层次结构,和3)利用任务层次结构中的分析工具。此外,我们确定的影响,以适应迭代细化,上下文,可变性和不确定性在框架的所有阶段。我们描述了一个用例,它简化了我们的抽象框架,展示了上下文如何影响出处层次结构,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。
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.
DOI: --
发表时间: 1994
期刊: Int. J. Hum. Comput. Stud.
影响因子: --
作者:
A. Bisantz;K. J. Vicente
通讯作者: K. J. Vicente
DOI: --
发表时间: 2007
期刊:
影响因子: --
作者:
作田絵里;Islam Saiful;山内清語;喜多村昇;作田 絵里;作田 絵里;作田 絵里;作田 絵里
通讯作者: 作田 絵里