TY - GEN
T1 - Finding optimal team for multi-skill task in spatial crowdsourcing
AU - Tao, Qian
AU - Du, Bowen
AU - Song, Tianshu
AU - Xu, Ke
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - These days, Online To Offline (O2O) platforms have been developing rapidly because of the popularization of smart phones and Mobile Internet. Spatial crowdsourcing, a burgeoning area in O2O market, is gaining more and more attention. It is a typical spatial crowdsourcing scenario in which an employer publishes a task and some workers will help him or her to accomplish it. However, most of previous work only considers the spatial information of workers and tasks, but ignores the individual variations among workers. In this paper, we raise a new problem called Software Development Team Formation (SDTF) problem, which aims to find a team of workers whose ability satisfies the requirement of the task. After showing the problem is NP-hard, we propose three greedy algorithms to approximately solve the problem. Besides, extensive experiments are conducted on synthetic and real datasets, which verify the effectiveness and efficiency of our algorithms.
AB - These days, Online To Offline (O2O) platforms have been developing rapidly because of the popularization of smart phones and Mobile Internet. Spatial crowdsourcing, a burgeoning area in O2O market, is gaining more and more attention. It is a typical spatial crowdsourcing scenario in which an employer publishes a task and some workers will help him or her to accomplish it. However, most of previous work only considers the spatial information of workers and tasks, but ignores the individual variations among workers. In this paper, we raise a new problem called Software Development Team Formation (SDTF) problem, which aims to find a team of workers whose ability satisfies the requirement of the task. After showing the problem is NP-hard, we propose three greedy algorithms to approximately solve the problem. Besides, extensive experiments are conducted on synthetic and real datasets, which verify the effectiveness and efficiency of our algorithms.
KW - Spatial crowdsourcing
KW - Task assignment
KW - Team formation
UR - https://www.scopus.com/pages/publications/85034610714
U2 - 10.1007/978-3-319-69781-9_18
DO - 10.1007/978-3-319-69781-9_18
M3 - 会议稿件
AN - SCOPUS:85034610714
SN - 9783319697802
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 185
EP - 194
BT - Web and Big Data - APWeb-WAIM 2017 International Workshops
A2 - Moon, Yang-Sae
A2 - Song, Shaoxu
A2 - Renz, Matthias
PB - Springer Verlag
T2 - 1st Asia-Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2017 held in Conjuction with the International Workshop on Mobile Web Data Analytics, MWDA 2017, International Workshop on Hot Topics in Big Spatial Data and Urban Computing, HotSpatial 2017, International Workshop on Graph Data Management and Analysis, GDMA 2017, 2nd International Workshop on Data Driven Crowdsourcing, DDC 2017, 2nd International Workshop on Spatio-temporal Data Management and Analytics, SDMA 2017 and International Workshop on Mobility Analytics from Spatial and Social Data, MASS 2017
Y2 - 7 July 2017 through 9 July 2017
ER -