Data Analytics and Visualization of Adaptive Collaboration Simulations

被引：4

作者：

Wachowiak-Smolikova, Renata ^{[1
]}

Zhu, Haibin ^{[1
]}

机构：

[1] Nipissing Univ, Dept Comp Sci & Math, North Bay, ON P1B 8L7, Canada

来源：

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS | 2023年 / 10卷 / 01期

基金：

加拿大自然科学与工程研究理事会;

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); VISUAL ANALYTICS; BIG DATA; SYSTEM;

D O I：

10.1109/TCSS.2022.3146049

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：84 / 93

页数：10

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