Abstract

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.

Original languageEnglish (US)
Title of host publicationCIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages2485-2487
Number of pages3
Volume24-28-October-2016
ISBN (Electronic)9781450340731
DOIs
StatePublished - Oct 24 2016
Event25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States
Duration: Oct 24 2016Oct 28 2016

Other

Other25th ACM International Conference on Information and Knowledge Management, CIKM 2016
CountryUnited States
CityIndianapolis
Period10/24/1610/28/16

Fingerprint

Team performance
Replacement
Prototype
Shrinkage
Scenarios
Experiment
Field study
Front-end

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

Cite this

Li, L., Tong, H., Cao, N., Ehrlich, K., Lin, Y. R., & Buchler, N. (2016). TEAMOPT: Interactive team optimization in big networks. In CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management (Vol. 24-28-October-2016, pp. 2485-2487). Association for Computing Machinery. https://doi.org/10.1145/2983323.2983340

TEAMOPT : Interactive team optimization in big networks. / Li, Liangyue; Tong, Hanghang; Cao, Nan; Ehrlich, Kate; Lin, Yu Ru; Buchler, Norbou.

CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management. Vol. 24-28-October-2016 Association for Computing Machinery, 2016. p. 2485-2487.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Li, L, Tong, H, Cao, N, Ehrlich, K, Lin, YR & Buchler, N 2016, TEAMOPT: Interactive team optimization in big networks. in CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management. vol. 24-28-October-2016, Association for Computing Machinery, pp. 2485-2487, 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, United States, 10/24/16. https://doi.org/10.1145/2983323.2983340
Li L, Tong H, Cao N, Ehrlich K, Lin YR, Buchler N. TEAMOPT: Interactive team optimization in big networks. In CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management. Vol. 24-28-October-2016. Association for Computing Machinery. 2016. p. 2485-2487 https://doi.org/10.1145/2983323.2983340
Li, Liangyue ; Tong, Hanghang ; Cao, Nan ; Ehrlich, Kate ; Lin, Yu Ru ; Buchler, Norbou. / TEAMOPT : Interactive team optimization in big networks. CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management. Vol. 24-28-October-2016 Association for Computing Machinery, 2016. pp. 2485-2487
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