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Vis Inf  2018, Vol. 2 Issue (4): 235-253    DOI: 10.1016/j.visinf.2018.12.004
论文     
面向表格式工业数据集的探索性分析工具综述
Aindrila Ghosha, Mona Nashaata, James Millera, Shaikh Quaderb, Chad Marstonc
aDepartment of Electrical and Computer Engineering, University of Alberta, 116 Street NW, Edmonton, T6G 1H9, Canada
bMachine Learning Research, IBM Canada, Toronto, Canada
cInformation Technology and Analytics, IBM U.S., Boston, United States
A comprehensive review of tools for exploratory analysis of tabular industrial datasets
Aindrila Ghosha, Mona Nashaata, James Millera, Shaikh Quaderb, Chad Marstonc  
aDepartment of Electrical and Computer Engineering, University of Alberta, 116 Street NW, Edmonton, T6G 1H9, Canada
bMachine Learning Research, IBM Canada, Toronto, Canada
cInformation Technology and Analytics, IBM U.S., Boston, United States
 全文: PDF 
摘要: 探索性数据分析对深刻理解数据有重要作用。在过去的20年里,研究人员提出了多种可视化的数据探索工具,可以介入到分析过程的每一步。然而,近年来,数据分析的需求发生了显著变化。随着数据的规模和类型不断增加,可扩展性和分析的持续时间成为研究人员主要关注的问题。此外,为了最大限度地降低分析成本,企业需要在分析知识有限的情况下可供使用的数据分析工具。为了应对这些挑战,传统的数据探索工具在过去几年中不断发展。  本文通过对工业表格数据集进行深入分析,确定了对大型数据集进行探索性分析的一组额外需求。随后对新兴的探索性数据分析领域的最新进展进行了全面的综述,研究了50种学术和非学术的可视数据探索工具,考察它们在探索性数据分析过程的六个基本步骤中的实用性,检验这些探索工具能够在多大程度上满足分析大型数据集的额外需求。最后,给出了可视化探索性数据分析领域的若干研究方向。
关键词: 探索性数据分析工业表格数据交互式可视化系统性文献综述有待研究的问题    
Abstract: Exploratory data analysis plays a major role in obtaining insights from data. Over the last two decades, researchers have proposed several visual data exploration tools that can assist with each step of the analysis process. Nevertheless, in recent years, data analysis requirements have changed significantly. With constantly increasing size and types of data to be analyzed, scalability and analysis duration are now among the primary concerns of researchers. Moreover, in order to minimize the analysis cost, businesses are in need of data analysis tools that can be used with limited analytical knowledge. To address these challenges, traditional data exploration tools have evolved within the last few years. In this paper, with an in-depth analysis of an industrial tabular dataset, we identify a set of additional exploratory requirements for large datasets. Later, we present a comprehensive survey of the recent advancements in the emerging field of exploratory data analysis. We investigate 50 academic and non-academic visual data exploration tools with respect to their utility in the six fundamental steps of the exploratory data analysis process. We also examine the extent to which these modern data exploration tools fulfill the additional requirements for analyzing large datasets. Finally, we identify and present a set of research opportunities in the field of visual exploratory data analysis.
Key words: Exploratory data analysis    Industrial tabular data    Interactive visualization    Systematic literature review    Research opportunities
出版日期: 2019-01-09
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Aindrila Ghosh
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引用本文:

Aindrila Ghosh, Mona Nashaat, James Miller, Shaikh Quader, Chad Marston. A comprehensive review of tools for exploratory analysis of tabular industrial datasets . Vis Inf, 2018, 2(4): 235-253.

链接本文:

http://www.zjujournals.com/vi/CN/10.1016/j.visinf.2018.12.004        http://www.zjujournals.com/vi/CN/Y2018/V2/I4/235

[1] Shixia Liu, Xiting Wang, Mengchen Liu, Jun Zhu. 机器学习模型的可视分析[J]. Vis Inf, 2017, 1(1): 48-56.