TY - GEN
T1 - TEAMOPT
T2 - 25th ACM International Conference on Information and Knowledge Management, CIKM 2016
AU - Li, Liangyue
AU - Tong, Hanghang
AU - Cao, Nan
AU - Ehrlich, Kate
AU - Lin, Yu Ru
AU - Buchler, Norbou
N1 - Funding Information:
This work is partially supported by the National Science Foundation under Grant No. IIS1017415, by DTRA under the grant number HDTRA1-16-0017, by Army Research Office under the contract number W911NF-16-1-0168, by National Institutes of Health under the grant number R01LM011986, Region II University Transportation Center under the project number 49997-3325 and a Baidu gift.
Publisher Copyright:
© 2016 Copyright held by the owner/author(s).
PY - 2016/10/24
Y1 - 2016/10/24
N2 - The science of team science is a rapidly emerging research field that studies strategies to understand and enhance the process and outcomes of collaborative, team-based research. An interesting research question we address in this work is how to maintain and optimize the team performance should certain changes happen to the team. In particular, we take the network approach to understanding the teams and consider optimizing the teams with several operations (e.g., replacement, expansion, shrinkage). We develop TeamOpt, a system to assist users in optimizing the team performance interactively to support the changes to a team. TeamOpt takes as input a large network of individuals (e.g., co-author network of researchers) and is able to assist users in assembling a team with specific requirements and optimizing the team in response to the changes made to the team. It is effective in finding the best candidates, and interactive with users' feedback in the loop. The system is developed using HTML5, JavaScript, D3.js (front-end) and Python CGI (back-end). A prototype system is already deployed. We will invite the audience to experiment with our TeamOpt in terms of its effectiveness, efficiency and applicability to various scenarios.
AB - The science of team science is a rapidly emerging research field that studies strategies to understand and enhance the process and outcomes of collaborative, team-based research. An interesting research question we address in this work is how to maintain and optimize the team performance should certain changes happen to the team. In particular, we take the network approach to understanding the teams and consider optimizing the teams with several operations (e.g., replacement, expansion, shrinkage). We develop TeamOpt, a system to assist users in optimizing the team performance interactively to support the changes to a team. TeamOpt takes as input a large network of individuals (e.g., co-author network of researchers) and is able to assist users in assembling a team with specific requirements and optimizing the team in response to the changes made to the team. It is effective in finding the best candidates, and interactive with users' feedback in the loop. The system is developed using HTML5, JavaScript, D3.js (front-end) and Python CGI (back-end). A prototype system is already deployed. We will invite the audience to experiment with our TeamOpt in terms of its effectiveness, efficiency and applicability to various scenarios.
UR - http://www.scopus.com/inward/record.url?scp=84996564701&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84996564701&partnerID=8YFLogxK
U2 - 10.1145/2983323.2983340
DO - 10.1145/2983323.2983340
M3 - Conference contribution
AN - SCOPUS:84996564701
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 2485
EP - 2487
BT - CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
PB - Association for Computing Machinery
Y2 - 24 October 2016 through 28 October 2016
ER -