A comprehensive review of tools for exploratory analysis of tabular industrial datasets  

作　　者：Aindrila Ghosh Mona Nashaat James Miller Shaikh Quader Chad Marston 

机构地区：[1]Department of Electrical and Computer Engineering,University of Alberta,116 Street NW,Edmonton,T6G 1H9,Canada [2]Machine Learning Research,IBM Canada,Toronto,Canada [3]Information Technology and Analytics,IBM U.S.,Boston,United States

出　　处：《Visual Informatics》2018年第4期235-253,共19页可视信息学（英文）

摘　　要：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.

关 键 词：Exploratory data analysis Industrial tabular data Interactive visualization Systematic literature review Research opportunities 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]