Steering data quality with visual analytics:The complexity challenge

作     者：Shixia Liu Gennady Andrienko Yingcai Wu Nan Cao Liu Jiang Conglei Shi Yu-Shuen Wang Seokhee Hong 

作者机构：Tsinghua UniversityBeijingChina Fraunhofer Institute IAISSankt-AugustinGermany CityUniversity of LondonLondonUK Zhejiang UniversityZhejiangChina Tongji UniversityShanghaiChina AirbnbSan FranciscoCAUSA National Chiao-Tung UniversityHsinchuTaiwan University of SydneySydneyAustralia 

出 版 物：《Visual Informatics》 (可视信息学（英文）)

年 卷 期：2018年第2卷第4期

页      面：191-197页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research was funded by National Key R&D Program of China(No.SQ2018YFB100002) the National Natural Science Foundation of China(No.s 61761136020,61672308) Microsoft Research Asia,Fraunhofer Cluster of Excellence on"Cognitive Internet Technologies",EU through project Track&Know(grant agreement 780754) NSFC(61761136020) NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization(U1609217) Zhejiang Provincial Natural Science Foundation(LR18F020001) NSFC Grants 61602306 Fundamental Research Funds for the Central Universities 

主　　题：Data quality management Visual analytics Data cleansing 

摘      要：Data quality management,especially data cleansing,has been extensively studied for many years in the areas of data management and visual *** the paper,we first review and explore the relevant work from the research areas of data management,visual analytics and human-computer *** for different types of data such as multimedia data,textual data,trajectory data,and graph data,we summarize the common methods for improving data quality by leveraging data cleansing techniques at different analysis *** on a thorough analysis,we propose a general visual analytics framework for interactively cleansing ***,the challenges and opportunities are analyzed and discussed in the context of data and humans.