Progressive Visual Analytics Using Scagnostics and an Automatic Partitioning Variables Selection Method 


Vol. 45,  No. 8, pp. 801-806, Aug.  2018
10.5626/JOK.2018.45.8.801


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  Abstract

In this paper, we propose a visual analytics system that combines progressive visualization with a partitioning variables selection method, one of the analytic techniques based on a scagnostics concept. In order to overcome the problems of scalability and performance associated with the existing method, all of the interface elements are designed so as to update the analysis progress in real time. The interface consists of two parts: an overview of the scatterplots to be analyzed and a detailed view for exploring interesting scatterplots in detail. We introduce the design rationale of our system and present a data analysis scenario to show how users can effectively use the system.


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  Cite this article

[IEEE Style]

D. Shin, S. L’Yi, H. Song, J. Seo, "Progressive Visual Analytics Using Scagnostics and an Automatic Partitioning Variables Selection Method," Journal of KIISE, JOK, vol. 45, no. 8, pp. 801-806, 2018. DOI: 10.5626/JOK.2018.45.8.801.


[ACM Style]

DongHwa Shin, Sehi L’Yi, Hyunjoo Song, and Jinwook Seo. 2018. Progressive Visual Analytics Using Scagnostics and an Automatic Partitioning Variables Selection Method. Journal of KIISE, JOK, 45, 8, (2018), 801-806. DOI: 10.5626/JOK.2018.45.8.801.


[KCI Style]

신동화, 이세희, 송현주, 서진욱, "산점도 진단분석과 분할 변수 선택 기법을 활용한 점진적인 시각적 분석," 한국정보과학회 논문지, 제45권, 제8호, 801~806쪽, 2018. DOI: 10.5626/JOK.2018.45.8.801.


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