Proceedings of the 2017 2nd International Conference on Automation, Mechanical and Electrical Engineering (AMEE 2017)

Key Technologies of Urban Computing in Big Data Era

Authors
Zhonglin He
Corresponding Author
Zhonglin He
Available Online September 2017.
DOI
https://doi.org/10.2991/amee-17.2017.41How to use a DOI?
Keywords
big data; urban computing; smart city; intelligient decision making
Abstract

In Big Data era, it is difficult to build effecive intelligient analysis and decision support system as urban computing information resources are incomplete and the horizontal integration and contribution of information is seriously inadequate. Intelligient analysis and decision support system largely depends on the seamless connection of data fusion analysis and information presentation with real scenes. This paper presents the major issues that urban computing is facing. It analyzes three key technologies of urban computing in big data era, namely, the 3D spatial-temporal expression and establishment of urban scenes, the semantic computing and fusion of urban multi-modal data, and the on-the-spot analysis and decision-making of urban events.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 2nd International Conference on Automation, Mechanical and Electrical Engineering (AMEE 2017)
Series
Advances in Engineering Research
Publication Date
September 2017
ISBN
978-94-6252-393-7
ISSN
2352-5401
DOI
https://doi.org/10.2991/amee-17.2017.41How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Zhonglin He
PY  - 2017/09
DA  - 2017/09
TI  - Key Technologies of Urban Computing in Big Data Era
BT  - Proceedings of the 2017 2nd International Conference on Automation, Mechanical and Electrical Engineering (AMEE 2017)
PB  - Atlantis Press
SP  - 201
EP  - 204
SN  - 2352-5401
UR  - https://doi.org/10.2991/amee-17.2017.41
DO  - https://doi.org/10.2991/amee-17.2017.41
ID  - He2017/09
ER  -