Explainable data transformation recommendation for automatic visualization

Wu, Ziliang1; Chen, Wei1; Ma, Yuxin2; Xu, Tong1; Yan, Fan1; Lv, Lei1; Qian, Zhonghao1; Xia, Jiazhi3

2022-12-01

10.1631/FITEE.2200409

Frontiers of Information Technology & Electronic Engineering   Impact Factor & Quartile

2095-9184

2095-9230

Automatic visualization generates meaningful visualizations to support data analysis and pattern finding for novice or casual users who are not familiar with visualization design. Current automatic visualization approaches adopt mainly aggregation and filtering to extract patterns from the original data. However, these limited data transformations fail to capture complex patterns such as clusters and correlations. Although recent advances in feature engineering provide the potential for more kinds of automatic data transformations, the auto-generated transformations lack explainability concerning how patterns are connected with the original features. To tackle these challenges, we propose a novel explainable recommendation approach for extended kinds of data transformations in automatic visualization. We summarize the space of feasible data transformations and measures on explainability of transformation operations with a literature review and a pilot study, respectively. A recommendation algorithm is designed to compute optimal transformations, which can reveal specified types of patterns and maintain explainability. We demonstrate the effectiveness of our approach through two cases and a user study.

Data transformation Data transformation recommendation Automatic visualization Explainability TP391

SCI

English

Others

National Natural Science Foundation of China[62132017] ; Fundamental Research Fundsfor the Central Universities, China[226202200235]

Computer Science ; Engineering

Computer Science, Information Systems ; Computer Science, Software Engineering ; Engineering, Electrical & Electronic

WOS:000903188700001

ZHEJIANG UNIV PRESS

Web of Science

1.Zhejiang Univ, State Key Lab CAD & CG, Hangzhou 310058, Peoples R China
2.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China
3.Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Peoples R China

Wu, Ziliang,Chen, Wei,Ma, Yuxin,et al. Explainable data transformation recommendation for automatic visualization[J]. Frontiers of Information Technology & Electronic Engineering,2022.

Wu, Ziliang.,Chen, Wei.,Ma, Yuxin.,Xu, Tong.,Yan, Fan.,...&Xia, Jiazhi.(2022).Explainable data transformation recommendation for automatic visualization.Frontiers of Information Technology & Electronic Engineering.

Wu, Ziliang,et al."Explainable data transformation recommendation for automatic visualization".Frontiers of Information Technology & Electronic Engineering (2022).