A deep learning spatiotemporal prediction framework for mobile crowdsourced services

Ben Said, Ahmed, Erradi, Abdelkarim, Ghari Neiat, Azadeh and Bouguettaya, Athman 2019, A deep learning spatiotemporal prediction framework for mobile crowdsourced services, Mobile Networks and Applications, vol. 24, no. 3, pp. 1120-1133, doi: 10.1007/s11036-018-1105-0.

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Title A deep learning spatiotemporal prediction framework for mobile crowdsourced services
Author(s) Ben Said, Ahmed
Erradi, Abdelkarim
Ghari Neiat, AzadehORCID iD for Ghari Neiat, Azadeh orcid.org/0000-0001-7512-7143
Bouguettaya, Athman
Journal name Mobile Networks and Applications
Volume number 24
Issue number 3
Start page 1120
End page 1133
Total pages 15
Publisher Springer New York
Place of publication New York, N.Y.
Publication date 2019-06
ISSN 1383-469X
Summary This papers presents a deep learning-based framework to predict crowdsourced service availability spatially and temporally. A novel two-stage prediction model is introduced based on historical spatio-temporal traces of mobile crowdsourced services. The prediction model first clusters mobile crowdsourced services into regions. The availability prediction of a mobile crowdsourced service at a certain location and time is then formulated as a classification problem. To determine the availability duration of predicted mobile crowdsourced services, we formulate a forecasting task of time series using the Gramian Angular Field. We validated the effectiveness of the proposed framework through multiple experiments.
Language eng
DOI 10.1007/s11036-018-1105-0
Indigenous content off
Field of Research 0805 Distributed Computing
0999 Other Engineering
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2018, Springer Science+Business Media
Persistent URL http://hdl.handle.net/10536/DRO/DU:30145179

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