IEEE Systems, Man, and Cybernetics Magazine - January 2018 - 19

Problems previously
considered to be
too complicated for
solutions by computerbased systems can
now be well defined
and, finally, solved.

Role negotiation specifies the roles that are required in
collaboration, and it clarifies the requirement of data mining from big data resources. That is, from the result of role
negotiation, we know what we need to search for in data
sets. Only when we discover what we need does big data
become a valuable resource. On the other hand, big data
produces role relationships, role specifications, the structures of environments, and group structures from the sense
of the E-CARGO model. All of the information is a great
asset for collaboration research, especially RBC.
Agent evaluation can be versatile and broad. There are
many aspects for us to evaluate, and the evaluation of different aspects highly affects the optimal role assignment in the
sense of group performance. Data mining from big data produces indicators to evaluate agents on roles [6], and data
collaboration may provide more big data for evaluation.
Role assignment seems to be a topic that is irrelevant to
big data. In fact, there are many factors that need to be considered while performing role assignments. For example,
cooperation/conflict factors are one such important element.
Ignorance of such factors in assignment may lead to a significant decrease in the actual team performance while the roles
are played, and consideration of cooperation/conflict factors
is required while conducting role assignment. Big data is the
source for us to extract such cooperation/conflict factors.
Role execution and transfer can also acquire benefits from
big data, because big data provides patterns, rules, and conditions for role transfer and role execution.
In summary, big data brings in the fundamental components of collaborative systems. At the same time, data
collaboration creates real big data. It is valuable to
apply big data to collaboration research and to apply
collaborative technologies to data analysis to make big
data. The conclusion is that collaboration needs data,
and data require collaboration to be big data.

would like to thank Dr. Dongning Liu from Guandong University of Technology, China, for his discussion with me on
some of the ideas presented here.
About the Author
Haibin Zhu (haibinz@nipissingu.ca) is a full professor
and coordinator of the Computer Science Program and
founding director of the Collaborative Systems Laboratory
at Nipissing University, Canada. He is currently serving as
cochair of the Technical Committee of Distributed Intelligent Systems of the IEEE Systems, Man, and Cybernetics
Society. He is a founding researcher of role-based collaboration and adaptive collaboration and a Senior Member
of the IEEE.
References
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[2] Honeywell. (2011, July). Data collaboration turning data into decisions. Advanced
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Acknowledgments
Thanks go to Mike Brewes of Nipissing University, Canada,
for his assistance in proofreading this article. In addition, I

nisms," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 36, no. 4, pp. 578-589,
July 2006.

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IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE

http://www.digitalistmag.com/technologies/big-data/leveraging-big-data-redefine-collaboration-enterprise-01243242 http://www.digitalistmag.com/technologies/big-data/leveraging-big-data-redefine-collaboration-enterprise-01243242 https://www.honeywellprocess.com/library/marketing/whitepapers/HoneywellOperationalInsight_DataCollaboration_WP636.pdf https://www.honeywellprocess.com/library/marketing/whitepapers/HoneywellOperationalInsight_DataCollaboration_WP636.pdf http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation

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