Crowdsourced POI labelling: Location-aware result inference and task assignment

Hu, H, Zheng, Y, Bao, Z, Li, G, Feng, J and Cheng, R 2016, 'Crowdsourced POI labelling: Location-aware result inference and task assignment', in Proceedings of the 32nd IEEE International Conference on Data Engineering (ICDE 2016), Helsinki, Finland, 16-20 May 2016, pp. 61-72.


Document type: Conference Paper
Collection: Conference Papers

Title Crowdsourced POI labelling: Location-aware result inference and task assignment
Author(s) Hu, H
Zheng, Y
Bao, Z
Li, G
Feng, J
Cheng, R
Year 2016
Conference name ICDE 2016 32nd IEEE International Conference on Data Engineering
Conference location Helsinki, Finland
Conference dates 16-20 May 2016
Proceedings title Proceedings of the 32nd IEEE International Conference on Data Engineering (ICDE 2016)
Publisher IEEE
Place of publication United States
Start page 61
End page 72
Total pages 12
Abstract Identifying the labels of points of interest (POIs), aka POI labelling, provides significant benefits in location-based services. However, the quality of raw labels manually added by users or generated by artificial algorithms cannot be guaranteed. Such low-quality labels decrease the usability and result in bad user experiences. In this paper, by observing that crowdsourcing is a best-fit for computer-hard tasks, we leverage crowdsourcing to improve the quality of POI labelling. To our best knowledge, this is the first work on crowdsourced POI labelling tasks. In particular, there are two sub-problems: (1) how to infer the correct labels for each POI based on workers' answers, and (2) how to effectively assign proper tasks to workers in order to make more accurate inference for next available workers. To address these two problems, we propose a framework consisting of an inference model and an online task assigner. The inference model measures the quality of a worker on a POI by elaborately exploiting (i) worker's inherent quality, (ii) the spatial distance between the worker and the POI, and (iii) the POI influence, which can provide reliable inference results once a worker submits an answer. As workers are dynamically coming, the online task assigner judiciously assigns proper tasks to them so as to benefit the inference. The inference model and task assigner work alternately to continuously improve the overall quality. We conduct extensive experiments on a real crowdsourcing platform, and the results on two real datasets show that our method significantly outperforms state-of-the-art approaches.
Subjects Database Management
Global Information Systems
Keyword(s) crowdsourcing
spatial database
DOI - identifier 10.1109/ICDE.2016.7498229
Copyright notice © 2016 IEEE
ISBN 9781509021086
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Created: Thu, 14 Jul 2016, 08:26:00 EST by Catalyst Administrator
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