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Visualization in the preprocessing phase: Getting insights from enterprise professionals
Information Visualization ( IF 2.3 ) Pub Date : 2020-01-30 , DOI: 10.1177/1473871619896101
Alessandra Maciel Paz Milani 1 , Fernando V. Paulovich 2 , Isabel Harb Manssour 1
Affiliation  

The current information age has increasingly required organizations to become data-driven. However, analyzing and managing raw data is still a challenging part of the data mining process. Even though we can find interview studies proposing design implications or recommendations for future visualization solutions in the data mining scope, they cover the entire workflow and do not fully focus on the challenges during the preprocessing phase and on how visualization can support it. Moreover, they do not organize a final list of insights consolidating the findings of other related studies. Hence, to better understand the current practice of enterprise professionals in data mining workflows, in particular, during the preprocessing phase, and how visualization supports this process, we conducted semi-structured interviews with 13 data analysts. The discussion about the challenges and opportunities based on the responses of the interviewees resulted in a list of 10 insights. This list was compared with the closest related works, improving the reliability of our findings and providing background, as a consolidated set of requirements, for future visualization research articles applied to visual data exploration in data mining. Furthermore, we provide greater details on the profile of the data analysts, the main challenges they face, and the opportunities that arise while they are engaged in data mining projects in diverse organizational areas.

中文翻译:

预处理阶段的可视化:从企业专业人士那里获得洞察力

当前的信息时代越来越需要组织成为数据驱动的。然而,分析和管理原始数据仍然是数据挖掘过程中具有挑战性的部分。尽管我们可以找到为数据挖掘范围内的未来可视化解决方案提出设计启示或建议的访谈研究,但它们涵盖了整个工作流程,并没有完全关注预处理阶段的挑战以及可视化如何支持它。此外,他们没有组织一份最终的见解清单,以巩固其他相关研究的结果。因此,为了更好地了解企业专业人员在数据挖掘工作流中的当前实践,特别是在预处理阶段,以及可视化如何支持这一过程,我们对 13 位数据分析师进行了半结构化访谈。根据受访者的回答对挑战和机遇的讨论得出了一份包含 10 条见解的清单。该列表与最接近的相关工作进行了比较,提高了我们发现的可靠性,并为未来应用于数据挖掘中的可视化数据探索的可视化研究文章提供了背景,作为一组统一的要求。此外,我们更详细地介绍了数据分析师的概况、他们面临的主要挑战以及他们在不同组织领域从事数据挖掘项目时出现的机遇。作为一组统一的要求,用于未来可视化研究文章应用于数据挖掘中的可视化数据探索。此外,我们更详细地介绍了数据分析师的概况、他们面临的主要挑战以及他们在不同组织领域从事数据挖掘项目时出现的机会。作为一组统一的要求,用于未来可视化研究文章应用于数据挖掘中的可视化数据探索。此外,我们更详细地介绍了数据分析师的概况、他们面临的主要挑战以及他们在不同组织领域从事数据挖掘项目时出现的机会。
更新日期:2020-01-30
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