期刊
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
卷 10, 期 1, 页码 84-93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)
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.
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