显性知识的作用:知识辅助视觉分析的概念模型

P. Federico, Markus Wagner, A. Rind, Albert Amor-Amoros, S. Miksch, W. Aigner
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引用次数: 51

摘要

视觉分析(VA)旨在结合人类和计算机的优势进行有效的数据分析。在这一努力中,人类来自先前经验的隐性知识是一项重要的资产,可以被人类和计算机利用来改进分析过程。虽然VA环境开始包含形式化、存储和利用这些知识的特性,但是这些环境集成显式知识的机制和程度差异很大。此外,这类重要的虚拟价值环境从未被现有的虚拟价值理论所阐述。本文在van Wijk的可视化模型的概念基础上,提出了知识辅助人工智能的概念模型。我们应用该模型描述了文献中知识辅助增值的各种例子,并详细阐述了其中的三个例子。此外,我们还说明了该模型的使用,以比较不同的设计方案,并评估现有的方法与他们的知识的使用。最后,该模型可以启发设计者有效地利用显式知识生成新的虚拟现实环境。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Role of Explicit Knowledge: A Conceptual Model of Knowledge-Assisted Visual Analytics
Visual Analytics (VA) aims to combine the strengths of humans and computers for effective data analysis. In this endeavor, humans’ tacit knowledge from prior experience is an important asset that can be leveraged by both human and computer to improve the analytic process. While VA environments are starting to include features to formalize, store, and utilize such knowledge, the mechanisms and degree in which these environments integrate explicit knowledge varies widely. Additionally, this important class of VA environments has never been elaborated on by existing work on VA theory. This paper proposes a conceptual model of Knowledge-assisted VA conceptually grounded on the visualization model by van Wijk. We apply the model to describe various examples of knowledge-assisted VA from the literature and elaborate on three of them in finer detail. Moreover, we illustrate the utilization of the model to compare different design alternatives and to evaluate existing approaches with respect to their use of knowledge. Finally, the model can inspire designers to generate novel VA environments using explicit knowledge effectively.
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