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ADERSIM-IBM partnership in big data
| 1. | Disaster & Emergency Management, York University, Toronto, Ontario, M3J 1P3, Canada |
| 2. | Department of Mathematics and Statistics, York University, Toronto, Ontario, M3J 1P3, Canada |
This short notes announces the recent development of the Advanced Disaster, Emergency and Rapid Response Simulation Initiative, in collaboration with IBM-Canada. Focus is on the Big Data analytics techniques and the IBM's Intelligent Operations Centre for Emergency Management platform.
References:
| [1] |
N. Y. Armonk, As Hurricane Season Approaches, IBM and The Weather Company Collaborate on Emergency Management for Cities, New IBM Intelligent Operations Center for Emergency Management Uses Real-time Weather Data to Help Communities Predict and Prepare for Disasters, 2015. Available from: http://www-03.ibm.com/press/us/en/pressrelease/47160.wss. Google Scholar |
| [2] |
J. Huang, A. Asgary and J. Wu,
Advanced disaster, emergency and rapid response simulation (ADERSIM), Big Data and Information Analytics, 1 (2016), v-v.
doi: 10.3934/bdia.2016.1.1v. |
| [3] |
A. Amaye, K. Neville, and A. Pope, BigPromises: using organisational mindfulness to integrate big data in emergency management decision making, Journal of Decision Systems, 25 (2016), ISS. SUPl. Google Scholar |
| [4] |
Operational insight helps city leaders manage a safer, smarter city, Intelligent Operations Center for Smarter Cities, 2016. Available from: http://www-03.ibm.com/software/products/en/intelligent-operations-center. Google Scholar |
show all references
References:
| [1] |
N. Y. Armonk, As Hurricane Season Approaches, IBM and The Weather Company Collaborate on Emergency Management for Cities, New IBM Intelligent Operations Center for Emergency Management Uses Real-time Weather Data to Help Communities Predict and Prepare for Disasters, 2015. Available from: http://www-03.ibm.com/press/us/en/pressrelease/47160.wss. Google Scholar |
| [2] |
J. Huang, A. Asgary and J. Wu,
Advanced disaster, emergency and rapid response simulation (ADERSIM), Big Data and Information Analytics, 1 (2016), v-v.
doi: 10.3934/bdia.2016.1.1v. |
| [3] |
A. Amaye, K. Neville, and A. Pope, BigPromises: using organisational mindfulness to integrate big data in emergency management decision making, Journal of Decision Systems, 25 (2016), ISS. SUPl. Google Scholar |
| [4] |
Operational insight helps city leaders manage a safer, smarter city, Intelligent Operations Center for Smarter Cities, 2016. Available from: http://www-03.ibm.com/software/products/en/intelligent-operations-center. Google Scholar |
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Weidong Bao, Wenhua Xiao, Haoran Ji, Chao Chen, Xiaomin Zhu, Jianhong Wu. Towards big data processing in clouds: An online cost-minimization approach. Big Data & Information Analytics, 2016, 1 (1) : 15-29. doi: 10.3934/bdia.2016.1.15 |
| [16] |
Yang Yu. Introduction: Special issue on computational intelligence methods for big data and information analytics. Big Data & Information Analytics, 2017, 2 (1) : i-ii. doi: 10.3934/bdia.201701i |
| [17] |
Xiangmin Zhang. User perceived learning from interactive searching on big medical literature data. Big Data & Information Analytics, 2018 doi: 10.3934/bdia.2017019 |
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Yaguang Huangfu, Guanqing Liang, Jiannong Cao. MatrixMap: Programming abstraction and implementation of matrix computation for big data analytics. Big Data & Information Analytics, 2016, 1 (4) : 349-376. doi: 10.3934/bdia.2016015 |
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Shuhua Zhang, Xinyu Wang, Hua Li. Modeling and computation of water management by real options. Journal of Industrial & Management Optimization, 2018, 14 (1) : 81-103. doi: 10.3934/jimo.2017038 |
| [20] |
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