TY - GEN
T1 - Online formation of large tree-structured team
AU - Ding, Cheng
AU - Xia, Fan
AU - Gopakumar,
AU - Qian, Weining
AU - Zhou, Aoying
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Software projects are often divided into different components and groups of individuals are assigned to various parts of the project. The matching of modular components of the project with right set of individuals is a fundamental challenge in both commercial and open source software projects. However, most of the extant studies on team formation have only considered the problem of creating flat teams, i.e., teams without communities and central authorities. In this paper, we study the problem of forming a hierarchically structured team. We use tree structure to model both teams and task specifications and introduce the notion of sub-team. Next, we define local density to minimize communication costs in sub-teams. Then, two algorithms are proposed to address this team formation problem in bottom up and top down manners. Furthermore, sub-teams are pre-computed and indexed to facilitate online formation of large teams. Results of experiments with a large dataset suggest that the index based algorithm can achieve both good effectiveness and excellent efficiency.
AB - Software projects are often divided into different components and groups of individuals are assigned to various parts of the project. The matching of modular components of the project with right set of individuals is a fundamental challenge in both commercial and open source software projects. However, most of the extant studies on team formation have only considered the problem of creating flat teams, i.e., teams without communities and central authorities. In this paper, we study the problem of forming a hierarchically structured team. We use tree structure to model both teams and task specifications and introduce the notion of sub-team. Next, we define local density to minimize communication costs in sub-teams. Then, two algorithms are proposed to address this team formation problem in bottom up and top down manners. Furthermore, sub-teams are pre-computed and indexed to facilitate online formation of large teams. Results of experiments with a large dataset suggest that the index based algorithm can achieve both good effectiveness and excellent efficiency.
UR - https://www.scopus.com/pages/publications/85016446091
U2 - 10.1007/978-3-319-55705-2_9
DO - 10.1007/978-3-319-55705-2_9
M3 - 会议稿件
AN - SCOPUS:85016446091
SN - 9783319557045
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 118
EP - 132
BT - Database Systems for Advanced Applications - DASFAA 2017 International Workshops
A2 - Chang, Lijun
A2 - Trajcevski, Goce
A2 - Hua, Wen
A2 - Bao, Zhifeng
PB - Springer Verlag
T2 - International Workshops on Database Systems for Advanced Applications, DASFAA 2017, 4th International Workshop on Big Data Management and Service, BDMS 2017, 2nd Workshop on Big Data Quality Management, BDQM 2017, 4th International Workshop on Semantic Computing and Personalization, SeCoP 2017, 1st International Workshop on Data Management and Mining on MOOCs, DMMOOC 2017
Y2 - 27 March 2017 through 30 March 2017
ER -