alexa Big Data Driven Indicator Choosing For Disaster Response Capacity Shortage Assessment Of Urban Critical Infrastructure
ISSN: 2161-0487

Journal of Psychology & Psychotherapy
Open Access

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17th World Summit on Positive Psychology, Psychotherapy & Cognitive Behavioral Sciences
May 01-03, 2017 Toronto, Canada

Zhao-ge Liu, Liu Tian-chang and Li Xiang-yang
Harbin Institute of Technology, China
ScientificTracks Abstracts: J Psychol Psychother
DOI: 10.4172/2161-0487-C1-011
Abstract
Critical Infrastructure (CI) is a kind of complex and huge network system and its operating failure will cause serious impact on national interest. Indicators of CI disaster response capacity shortage assessment need to be systematic, objective and consistent and analyzing at the aspect of capacity shortage help us to grasp the weak spots and improve response capacity. This paper proposes a big data driven indicator choosing method for CI response capacity shortage assessment. With the big data driven method, we can obtain and grasp historical experience guiding the scenario designing and global indicator choosing of CI response capacity shortage assessment. Besides, we can identify potential basic indicators and guides the basic indicator choosing and improving by data analysis.
Biography

Zhao-ge Liu is currently a Master’s student in Harbin Institute of Technology in China. He has his expertise in Management Science. He focuses on emergency management, food safety and customer relationship management. He has participated in two projects of Natural Science Foundation of China.

Email: [email protected]

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