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PrAVA: Preprocessing profiling approach for visual analytics
Information Visualization ( IF 2.3 ) Pub Date : 2021-07-02 , DOI: 10.1177/14738716211021591
Alessandra Maciel Paz Milani 1, 2 , Lucas Angelo Loges 2 , Fernando Vieira Paulovich 3 , Isabel Harb Manssour 2
Affiliation  

To accommodate the demands of a data-driven society, we have expanded our ability to collect and store data, develop sophisticated algorithms, and generate elaborated visual representations of the data analysis process outcomes. However, data preprocessing, as the activity of transforming the raw data into an appropriate format for subsequent analysis, is still a challenging part of this process. Although we can find studies that address the use of visualization techniques to support the activities in the scope of preprocessing, the current Visual Analytics processes do not consider preprocessing an equally important phase in their processes. Hence, with this paper, we aim to contribute to the discussion of how we can incorporate the preprocessing as a prominent phase in the Visual Analytics process and promote better alternatives to assist the data analysts during the preprocessing activities. To achieve that, we are introducing the Preprocessing Profiling Approach for Visual Analytics (PrAVA), a conceptual Visual Analytics process that includes Preprocessing Profiling as a new phase. It also contemplates a set of guidelines to be considered by new solutions adopting PrAVA. Moreover, we analyze its applicability through use case scenarios that show resourceful methods for data understanding and evaluation of the preprocessing impacts. As a final contribution, we indicate a list of research opportunities in the scope of preprocessing combined with visualization and Visual Analytics to stimulate a shift to visual preprocessing.



中文翻译:

PrAVA:用于可视化分析的预处理分析方法

为了适应数据驱动型社会的需求,我们扩大了收集和存储数据的能力,开发了复杂的算法,并生成了数据分析过程结果的详细可视化表示。然而,数据预处理,作为将原始数据转换为适当格式以供后续分析的活动,仍然是该过程中具有挑战性的部分。尽管我们可以找到解决使用可视化技术来支持预处理范围内的活动的研究,但当前的可视化分析流程并未将预处理视为其流程中同等重要的阶段。因此,有了这篇论文,我们的目标是参与讨论如何将预处理作为可视化分析过程中的一个突出阶段,并在预处理活动中推广更好的替代方案来协助数据分析师。为了实现这一目标,我们引入了可视化分析预处理分析方法 (PrAVA),这是一个概念性的可视化分析过程,其中包括将预处理分析作为一个新阶段。它还考虑了一套指南,供采用 PrAVA 的新解决方案考虑。此外,我们通过用例场景分析其适用性,这些用例场景展示了用于数据理解和评估预处理影响的资源丰富的方法。作为最后的贡献,

更新日期:2021-07-04
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