4.6 Article

Data Analytics and Visualization of Adaptive Collaboration Simulations

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSS.2022.3146049

关键词

Collaboration; Data models; Data visualization; Analytical models; Adaptive systems; Adaptation models; Data analysis; Adaptive collaboration (AC); adaptive systems; big data; data analytics; dynamic modeling; role-based collaboration (RBC); visual analytics (VA)

向作者/读者索取更多资源

This article presents an adaptive computational methodology called Role-based Collaboration (RBC) that uses roles to facilitate and analyze system behavior in collaborative entities. The use of visualization provides insights into complex system behaviors in dynamic environments. The results show that time-varying systems can be optimized and assessed using visual analytics (VA) dashboards.
Role-based collaboration (RBC) is an adaptive computational methodology that uses roles as underlying mechanisms to facilitate and analyze system behavior for entities that collaborate and coordinate their activities with or within these systems. In dynamic environments, including those that occur in large-scale simulations, visualization provides insights into complex systems behaviors. This article presents a visual analytics (VA) approach to studying dynamics involved in adaptive collaboration (AC) for large, multiagent simulation model using new open-source tools. The results show that time-varying systems can be steered for optimal performance and assessing adaptations using VA dashboards.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据