Scalable Mobile Data Streaming with Trajectory Preserving Partitioning

With the rise of sensor technology, ubiquitous mobile devices generate high-rate spatio-temporal data streams and send to backend servers in real time. While the mobile data streams are critical sources of mobility analytics supporting advanced mobile services, they also raise new challenges of scal...

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Bibliographic Details
Published in2014 IEEE International Conference on Mobile Services pp. 16 - 23
Main Authors Xin Zhang, Guoqiang Hu, Ning Duan, Peng Gao, Weishan Dong, Jun Zhu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2014
Subjects
graph partition
Internet-of-Things
Joining processes
Mobile communication
mobile data streaming
Partitioning algorithms
Real-time systems
Roads
service scalability
Trajectory
Vehicles
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Summary:With the rise of sensor technology, ubiquitous mobile devices generate high-rate spatio-temporal data streams and send to backend servers in real time. While the mobile data streams are critical sources of mobility analytics supporting advanced mobile services, they also raise new challenges of scalable data processing for trajectory-featured data streams. In this paper, we develop a scalable framework for mobile data streaming and present its architectural strategy. By introducing the mobility localization theme for the core partition component, a geo-spatial partition method is proposed for high performance data stream dispatching. Experiments on real world data demonstrate the effectiveness of the proposed method.
ISSN:2329-6429
2329-6453
DOI:10.1109/MobServ.2014.12