Human-computer interaction based on semantic focus+context for information visualization

被引：0

作者：

Ren, Lei ^{[1
]}

Wei, Yong-Chang ^{[1
]}

Du, Yi ^{[2
]}

Zhang, Xiao-Long ^{[3
]}

Dai, Guo-Zhong ^{[4
]}

机构：

[1] School of Automation Science and Electrical Engineering, Beihang University, Beijing

[2] Scientific Data Center, Computer Network Information Center, Chinese Academy of Sciences, Beijing

[3] School of Information Sciences and Technology, Pennsylvania State University, 16802, PA

[4] Beijing Key Laboratory of Human-Computer Interaction, Institute of Software, Chinese Academy of Sciences, Beijing

来源：

Jisuanji Xuebao/Chinese Journal of Computers | 2015年 / 38卷 / 12期

基金：

中国国家自然科学基金;

关键词：

Big data; Focus +Context; Human-computer interaction; Information visualization; User interface; Visual analytics;

D O I：

10.11897/SP.J.1016.2015.02488

中图分类号：

学科分类号：

摘要：

Big data has become another revolutionary technology following the booming of cloud computing and internet of things. Information visualization is regarded as an essential approach and powerful tool for users to get insight from big data. However, great challenges still exist in information visualization and smart interaction in small interfaces according to cognitive law. This paper proposes a semantic Focus+Context interaction technology for information visualization in user interface. Firstly, a semantic distance model and a semantic Degree-Of-Interest (DOI) model towards information space and visual representation space are presented. And based on the models, semantic context related to a focus is defined. Secondly, the paper proposes a semantic Focus+Context based user interface model, defining both abstract and entity elements as well as the mappings in this kind of user interface. Finally, the proposed technology is applied to semantic theme clustering for exploration of large scale file systems. Application examples show that the proposed technology can effectively support large scale information visualization in small interface and intelligent interaction for semantic exploration of complex data. © 2015, Science Press. All right reserved.

引用

页码：2488 / 2499

页数：11

共 35 条

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