Exploiting history to reduce interaction costs in collaborative analysis
Exploiting history to reduce interaction costs in collaborative analysis
复制标题
利用历史记录来降低协作分析中的交互成本
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Melanie Tory
中科院分区：
文献类型：
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作者：
Ali Sarvghad;Melanie Tory
当分析师以分布式方式工作时，他们需要了解他们的合作者做了什么，以及哪些分析途径尚未调查。尽管可视化历史有可能传达此类信息，但常见的表示通常仅限于过去工作的顺序列表。这样的表示不容易理解维空间的分析覆盖（即哪些维已经被研究过，哪些没有）。这使得分析师很难计划他们的下一步，特别是当维度的数量很大时。在本文中，我们建议从维度覆盖的角度来表示先验分析。维度视图提供了一个独特的视角，通过使分析师能够轻松地识别已检查的维度以及组合，可以促进探索性分析。我们假设，添加此视图到可视化历史的常见表示将通过帮助分析师发现要探索的数据子集来降低认知和交互成本。我们研究了分布式协同可视化过程中，这种观点的影响。我们的研究结果表明，提供维度和数据空间的视图可以减少识别和调查未探索区域所需的时间，并提高这种理解的准确性。此外，提供这些视图会导致整个维度空间的更大覆盖范围。
When analysts work in a distributed fashion, they need to understand what their collaborators have done and what avenues of analysis remain uninvestigated. Although visualization history has the potential to communicate such information, the common representations are often limited to sequential lists of past work. Such representations do not make it easy to understand the analytic coverage of the dimension space (i.e. which dimensions have been investigated and which have not). This makes it difficult for an analyst to plan their next steps, particularly when the number of dimensions is large. In this paper, we propose representing the prior analysis from a dimension coverage perspective. Dimension view provides a unique perspective that can facilitate exploratory analysis by enabling analysts to easily identify what dimensions have been examined and in what combinations. We hypothesize that addition of this view to common representations of visualization history will reduce cognitive and interaction costs by helping the analyst to discover data subsets to explore. We studied the effects of this view on a distributed collaborative visualization process. Our findings show that providing views of the dimension and data space reduces time required for identifying and investigating unexplored regions and increases the accuracy of this understanding. In addition, providing these views results in a larger coverage of entire dimension space.