Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
4-2020
Abstract
With the rapid development of mobile networks and the widespread usage of mobile devices, spatial crowdsourcing, which refers to assigning location-based tasks to moving workers, has drawn increasing attention. One of the major issues in spatial crowdsourcing is task assignment, which allocates tasks to appropriate workers. However, existing works generally assume the static offline scenarios, where the spatio-temporal information of all the workers and tasks is determined and known a priori. Ignorance of the dynamic spatio-temporal distributions of workers and tasks can often lead to poor assignment results. In this work we study a novel spatial crowdsourcing problem, namely Predictive Task Assignment (PTA), which aims to maximize the number of assigned tasks by taking into account both current and future workers/tasks that enter the system dynamically with location unknown in advance. We propose a two-phase data-driven framework. The prediction phase hybrids different learning models to predict the locations and routes of future workers and designs a graph embedding approach to estimate the distribution of future tasks. In the assignment component, we propose both greedy algorithm for large-scale applications and optimal algorithm with graph partition based decomposition. Extensive experiments on two real datasets demonstrate the effectiveness of our framework.
Keywords
prediction, task assignment, spatial crowdsourcing
Discipline
Databases and Information Systems | Data Science
Research Areas
Data Science and Engineering
Publication
2020 36th IEEE International Conference on Data Engineering (ICDE): Dallas, Texas; April 20-24: Proceedings
First Page
13
Last Page
24
ISBN
9781728129037
Identifier
10.1109/ICDE48307.2020.00009
Publisher
IEEE Computer Society
City or Country
Los Alamitos, CA
Citation
ZHAO, Yan; ZHENG, Kai; CUI, Yue; SU, Han; ZHU, Feida; and ZHOU, Xiaofang.
Predictive task assignment in spatial crowdsourcing: A data-driven approach. (2020). 2020 36th IEEE International Conference on Data Engineering (ICDE): Dallas, Texas; April 20-24: Proceedings. 13-24.
Available at: https://ink.library.smu.edu.sg/sis_research/5652
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/ICDE48307.2020.00009