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Enhancing Knowledge Management and Decision-Making Capability of China’s Emergency Operations Center Using Big Data

Yefeng Maa, Hui Zhangb

a School of Economics and Management, Tsinghua University, Beijing, China;
b Department of Engineering Physics, Tsinghua University, Beijing, China

* Corresponding Author: Hui Zhang, email

Intelligent Automation & Soft Computing 2018, 24(1), 107-114. https://doi.org/10.1080/10798587.2016.1267249

Abstract

Emerging communication and computing technologies such as social media, Internet of Things and big data provide great opportunities to improve information management systems for emergency operations. This paper studies the issues of information management at China’s Emergency Operations Center (EOC), and proposes a data-driven knowledge management system (KMS) to support decisionmaking, coordination, and collaboration within EOCs and with the public. In the proposed KMS, big data analytics is employed to gather and analyze information from different knowledge domains and track how a crisis evolves in physical world and in cyber space. The proposed system aims at improving situation awareness of public opinions and regulating human behaviors in regards to an emergency. A case study is presented to explain how the proposed system is applied to improve decision-making during emergency.

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Cite This Article

APA Style
Ma, Y., Zhang, H. (2018). Enhancing knowledge management and decision-making capability of china’s emergency operations center using big data. Intelligent Automation & Soft Computing, 24(1), 107-114. https://doi.org/10.1080/10798587.2016.1267249
Vancouver Style
Ma Y, Zhang H. Enhancing knowledge management and decision-making capability of china’s emergency operations center using big data. Intell Automat Soft Comput . 2018;24(1):107-114 https://doi.org/10.1080/10798587.2016.1267249
IEEE Style
Y. Ma and H. Zhang, “Enhancing Knowledge Management and Decision-Making Capability of China’s Emergency Operations Center Using Big Data,” Intell. Automat. Soft Comput. , vol. 24, no. 1, pp. 107-114, 2018. https://doi.org/10.1080/10798587.2016.1267249



cc Copyright © 2018 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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