VAET: A Visual Analytics Approach for E-transactions Time-Series

被引：44

作者：

Xie, Cong ^{[1
]}

Chen, Wei ^{[1
,2
]}

Huang, Xinxin ^{[1
]}

Hu, Yueqi ^{[3
]}

Barlowe, Scott ^{[4
]}

Yang, Jing ^{[3
]}

机构：

[1] Zhejiang Univ, State Key Lab CAD&CG, Hangzhou, Zhejiang, Peoples R China

[2] Zhejiang Univ, Cyber Innovat Joint Res Ctr, Hangzhou, Zhejiang, Peoples R China

[3] Univ N Carolina, Dept Comp Sci, Charlotte, NC 28223 USA

[4] Western Carolina Univ, Cullowhee, NC USA

来源：

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS | 2014年 / 20卷 / 12期

基金：

中国国家自然科学基金; 国家自然科学基金重大项目; 国家教育部博士点专项基金资助; 国家高技术研究发展计划(863计划);

关键词：

Time-Series; Visual Analytics; E-transaction; VISUALIZATION;

D O I：

10.1109/TVCG.2014.2346913

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Previous studies on E-transaction time-series have mainly focused on finding temporal trends of transaction behavior. Interesting transactions that are time-stamped and situation-relevant may easily be obscured in a large amount of information. This paper proposes a visual analytics system, Visual Analysis of E-transaction Time-Series (VAET), that allows the analysts to interactively explore large transaction datasets for insights about time-varying transactions. With a set of analyst-determined training samples, VAET automatically estimates the saliency of each transaction in a large time-series using a probabilistic decision tree learner. It provides an effective time-of-saliency (TOS) map where the analysts can explore a large number of transactions at different time granularities. Interesting transactions are further encoded with KnotLines, a compact visual representation that captures both the temporal variations and the contextual connection of transactions. The analysts can thus explore, select, and investigate knotlines of interest. A case study and user study with a real E-transactions dataset (26 million records) demonstrate the effectiveness of VAET.

引用

页码：1743 / 1752

页数：10

共 27 条

[1]

[Anonymous], 2011, HUM COMPUT INT SPRIN, DOI DOI 10.1007/978-0-85729-079-3_1

[2]

[Anonymous], 2012, Mining of massive datasets

[3]

Cadez I. V., 2001, KDD-2001. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, P37, DOI 10.1145/502512.502523

[4] WireVis: Visualization of categorical, time-varying data from financial transactions [J].

Chang, Remco ;

Ghoniem, Mohammad ;

Kosara, Robert ;

Ribarsky, William ;

Yang, Jing ;

Suma, Evan ;

Ziemkiewicz, Caroline ;

Kern, Daniel ;

Sudjianto, Agus .

VAST: IEEE SYMPOSIUM ON VISUAL ANALYTICS SCIENCE AND TECHNOLOGY 2007, PROCEEDINGS, 2007, :155-+

[5]

Guo DS, 2006, IEEE T VIS COMPUT GR, V12, P1461, DOI 10.1109/TVCG.2006.84

[6]

Hall M., 2009, SIGKDD Explorations, V11, P10, DOI DOI 10.1145/1656274.1656278

[7] Density displays for data stream monitoring [J].

Hao, Ming ;

Keim, Daniel A. ;

Dayal, Umeshwar ;

Oelke, Daniela ;

Tremblay, Chantal .

COMPUTER GRAPHICS FORUM, 2008, 27 (03) :895-902

[8]

Hao Ming C, 2001, VISUALIZATION DIRECT

[9]

Hochheiser H., 2004, Information Visualization, V3, P1, DOI 10.1057/palgrave.ivs.9500061

[10] Graphical Perception of Multiple Time Series [J].

Javed, Wages ;

McDonnel, Bryan ;

Elmqvist, Niklas .

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2010, 16 (06) :927-934

← 1 2 3 →