Please wait a minute...
Vis Inf
Vis Inf  2018, Vol. 2 Issue (4): 191-197    DOI: 10.1016/j.visinf.2018.12.001
论文     
运用可视分析控制数据质量：数据复杂性挑战
Shixia Liua, Gennady Andrienkob,c,Yingcai Wud, Nan Caoe, Liu Jianga, Conglei Shif, Yu-Shuen Wangg, Seokhee Hongh
aTsinghua University, Beijing, China
bFraunhofer Institute IAIS, Sankt-Augustin, Germany
cCity, University of London, London, UK
dZhejiang University, Zhejiang, China
eTongji University, Shanghai, China
fAirbnb, San Francisco, CA, USA
gNational Chiao-Tung University, Hsinchu, Taiwan
hUniversity of Sydney, Sydney, Australia
Steering data quality with visual analytics: The complexity challenge
Shixia Liua, Gennady Andrienkob,c,Yingcai Wud, Nan Caoe, Liu Jianga, Conglei Shif, Yu-Shuen Wangg, Seokhee Hongh
aTsinghua University, Beijing, China
bFraunhofer Institute IAIS, Sankt-Augustin, Germany
cCity, University of London, London, UK
dZhejiang University, Zhejiang, China
eTongji University, Shanghai, China
fAirbnb, San Francisco, CA, USA
gNational Chiao-Tung University, Hsinchu, Taiwan
hUniversity of Sydney, Sydney, Australia
 全文: PDF 
摘要： 多年来，数据质量管理，尤其是数据清洗，已在数据管理和可视分析领域得到广泛研究。本文将首先回顾和梳理数据管理、可视分析和人机交互领域相关的研究工作。然后，针对不同类型的数据，如多媒体数据、文本数据、轨迹数据和图形数据，总结在不同分析阶段利用数据清洗技术提高数据质量的常用方法。通过全面的分析，提出了一个通用的交互清洗数据的可视分析框架。最后，从数据和人的角度分析和讨论了面对的挑战和机遇。
关键词： 数据质量管理;  可视分析;  数据清洗    
Abstract: Data quality management, especially data cleansing, has been extensively studied for many years in the areas of data management and visual analytics. In the paper, we first review and explore the relevant work from the research areas of data management, visual analytics and human computer interaction. Then 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 stages. Based on a thorough analysis, we propose a general visual analytics framework for interactively cleansing data. Finally, the challenges and opportunities are analyzed and discussed in the context of data and humans.
Key words: Data quality management    Visual analytics    Data cleansing
出版日期: 2019-01-14
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章　　
Shixia Liu
Gennady Andrienko
Yingcai Wu
Nan Cao
Liu Jiang
Conglei Shi
Yu-Shuen Wang
Seokhee Hong

引用本文:

Shixia Liu, Gennady Andrienko, Yingcai Wu, Nan Cao, Liu Jiang, Conglei Shi, Yu-Shuen Wang, Seokhee Hong. Steering data quality with visual analytics: The complexity challenge . Vis Inf, 2018, 2(4): 191-197.

链接本文:

http://www.zjujournals.com/vi/CN/10.1016/j.visinf.2018.12.001    或    http://www.zjujournals.com/vi/CN/Y2018/V2/I4/191

[1] Jenny Vuong, Sandeep Kaur, Julian Heinrich, Bosco K.Ho, Christopher J.Hammang, Benedetta F.Baldi, Seán I.O’Donoghue. Versus——使用2AFC方法评估可视化和图像质量的工具[J]. Vis Inf, 2018, 2(4): 225-234.
[2] Maha El Meseery, Orland Hoeber. 地理协同平行坐标（GCPC）：环境数据分析的现场试验研究 [J]. Vis Inf, 2018, 2(2): 111-124.
[3] WeiZeng, Chi-Wing Fu, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, Kwan-Liu Ma.

城市人日常出行数据中所含规律的可视分析 [J]. Vis Inf, 2017, 1(2): 132-142.