Researchers have devised many theoretical models for specifying users' objectives, tasks, and insights as they interact with a visualization system. These representations are essential for understanding the insight discovery process, such as when inferring user interaction patterns that lead to insight or assessing the rigor of reported insights. However, many theoretical models can be notoriously difficult to translate into code, limiting their applicability across multiple studies. This paper calls attention to the consistent structures that recur across the visualization literature and describes how they connect multiple theoretical representations of insight. We present a toolkit called Pyxis that makes it easy to specify insights, tasks, and objectives using these consistent structures, enabling a wider audience of researchers and developers to adopt the corresponding models. Furthermore, our evaluation scenarios show that Pyxis enables a broader and deeper understanding of the structure and properties of insight compared to theoretical representations alone.
翻译:研究人员设计了许多理论模型,用以说明用户与直观系统互动时的目标、任务和见解,这些表述对于了解洞见发现过程至关重要,例如,在推断用户互动模式从而导致深入了解或评估所报告洞见的严谨程度时,这些表达方式对于了解洞察过程至关重要,然而,许多理论模型可能很难被臭名昭著地转化为代码,限制了其在多个研究中的适用性。本文件提请注意视觉文献中反复出现的一致结构,并说明了它们如何将多种洞察理论表达联系起来。我们提出了一个称为“Pyxis”的工具包,它便于利用这些一致的结构来说明洞察、任务和目标,使更多的研究人员和开发人员能够采用相应的模型。此外,我们的评估情景表明,Pyxis能够更广泛和更深入地了解洞察结构和特性,而光是理论陈述。