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Finding Optimal Team for Multiskill Task Based on Vehicle Sensors Data
Joint Authors
Du, Bowen
Tao, Qian
Zhu, Feng
Song, Tianshu
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-31
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
These days, with the increasingly widespread employment of sensors, particularly those attached to vehicles, the collection of spatial data is becoming easier and more accurate.
As a result, many relevant areas, such as spatial crowdsourcing, are gaining ever more attention.
A typical spatial crowdsourcing scenario involves an employer publishing a task and some workers helping to accomplish it.
However, most of previous studies have only considered the spatial information of workers and tasks, while ignoring individual variations among workers.
In this paper, we consider the Software Development Team Formation (SDTF) problem, which aims to assemble a team of workers whose abilities satisfy the requirements of the task.
After showing that the problem is NP-hard, we propose three greedy algorithms and a multiple-phase algorithm to approximately solve the problem.
Extensive experiments are conducted on synthetic and real datasets, and the results verify the effectiveness and efficiency of our algorithms.
American Psychological Association (APA)
Du, Bowen& Tao, Qian& Zhu, Feng& Song, Tianshu. 2017. Finding Optimal Team for Multiskill Task Based on Vehicle Sensors Data. Journal of Sensors،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1187541
Modern Language Association (MLA)
Du, Bowen…[et al.]. Finding Optimal Team for Multiskill Task Based on Vehicle Sensors Data. Journal of Sensors No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1187541
American Medical Association (AMA)
Du, Bowen& Tao, Qian& Zhu, Feng& Song, Tianshu. Finding Optimal Team for Multiskill Task Based on Vehicle Sensors Data. Journal of Sensors. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1187541
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1187541