Big Data Analytics Framework for Natural Disaster Management in Malaysia

Mohammad Fikry Abdullah, Mardhiah Ibrahim, Harlisa Zulkifli

2017

Abstract

Decision making in natural disaster management has its own challenge that needs to be tackled. In times of disaster, government as a response organisation must conduct timely and accurate decisions to ensure rapid assistance and effective recovery for the victim involved can be conducted. The aim of this paper is to embark strategic decision making in government concerning to disaster management through Big Data Analytics (BDA) approach. BDA technology is integrated as a solution to manage, utilise, maximise, and expose insight of climate change data for dealing water related natural disaster. NAHRIM as a government agency responsible in conducting research on water and its environment proposed a BDA framework for natural disaster management using NAHRIM historical and simulated projected hydroclimate datasets. The objective of developing this framework is to assist the government in making decisions concerning disaster management by fully utilised NAHRIM datasets. The BDA framework that consists of three stages; Data Acquisition, Data Computation, and Data Interpretation and seven layers; Data Source, Data Management, Analysis, Data Visualisation, Disaster Management, and Decision is hoped to give impact in prevention, mitigation, preparation, adaptation, response and recovery of water related natural disasters.

References

  1. Alhawari, S., Karadsheh, L., Talet, A.N. and Mansour, E., 2012. Knowledge-based risk management framework for information technology project. International Journal of Information Management, 32(1), pp.50-65.
  2. Ali, R.H.R.M., Mohamad, R. and Sudin, S., 2016, August. A proposed framework of big data readiness in public sectors. In F.A.A. Nifa, M.N.M. Nawi and A. Hussain eds.,, AIP Conference Proceedings (Vol. 1761, No. 1, p. 020089). AIP Publishing.
  3. Amin, M.Z.M. (2016). Applying Big Data Analytics (BDA) to Diagnose Hydrometeorological Related Risk Due To Climate Change. GeoSmart Asia, [online] Available at: http://geosmartasia.org/presentation/applying-bigdata-analytics-BDA-to-diagnose-hydrometeorological-related-risk-due-to-climate-change.pdf [Accessed 1 November 2016]
  4. Emmanouil, D. and Nikolaos, D., Big data analytics in prevention, preparedness, response and recovery in crisis and disaster management. In The 18th International Conference on Circuits, Systems, Communications and Computers (CSCC 2015), Recent Advances in Computer Engineering Series (Vol. 32, pp. 476-482).
  5. Faghmous, J.H. and Kumar, V., 2014. A big data guide to understanding climate change: The case for theoryguided data science. Big data, 2(3), pp.155-163.
  6. Ford, J.D., Tilleard, S.E., Berrang-Ford, L., Araos, M., Biesbroek, R., Lesnikowski, A.C., MacDonald, G.K., Hsu, A., Chen, C. and Bizikova, L., 2016. Opinion: Big data has big potential for applications to climate change adaptation. Proceedings of the National Academy of Sciences, 113(39), pp.10729-10732.
  7. Hashem, I.A.T., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A. and Khan, S.U., 2015. The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, pp.98-115.
  8. Hu, H., Wen, Y., Chua, T.S. and Li, X., 2014. Toward scalable systems for big data analytics: A technology tutorial. IEEE Access, 2, pp.652-687.
  9. Jagadish, H.V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J.M., Ramakrishnan, R. and Shahabi, C., 2014. Big data and its technical challenges. Communications of the ACM, 57(7), pp.86- 94.
  10. Kaplan, Robert S., and Anette Mikes. 2012. Managing Risks: A New Framework. Harvard Business Review 90, no. 6.
  11. Kapucu, N. and Garayev, V., 2011. Collaborative decisionmaking in emergency and disaster management. International Journal of Public Administration, 34(6), pp.366-375.
  12. Kim, G.H., Trimi, S. and Chung, J.H., 2014. Big-data applications in the government sector. Communications of the ACM, 57(3), pp.78-85.
  13. Lifescale Analytics. (2015). Descriptive to Prescriptive Analysis : Accelerating Business Insights with Data Analytics. Lifescale Analytics, [online] Available at: http://www.lifescaleanalytics.com/lsahero9/applicati on/files/7114/3187/3188/leadbrief_descripprescrip_we b.pdf [Accessed 4 December 2016].
  14. MAMPU (2014). Public Sector Big Data Analytics Initiative: Malaysia's Perspective. MAMPU, [online] Available at: http://www.mampu.gov.my/ms/ penerbitan-mampu/send/100-forum-asean-cio-2014 /275-1-keynote-mampu [ Accessed 30 November 2016]
  15. Wang, L., Wang, G. and Alexander, C.A., 2015. Big data and visualization: methods, challenges and technology progress. Digital Technologies, 1(1), pp.33-38.
  16. Malomo, F. and Sena, V., 2016. Data Intelligence for Local Government? Assessing the Benefits and Barriers to Use of Big Data in the Public Sector. Policy & Internet.
  17. Othman, S.H. and Beydoun, G., 2013. Model-driven disaster management. Information & Management, 50(5), pp.218-228.
  18. Tekiner, F. and Keane, J.A., 2013, October. Big data framework. In Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on (pp. 1494- 1499). IEEE.
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Paper Citation


in Harvard Style

Abdullah M., Ibrahim M. and Zulkifli H. (2017). Big Data Analytics Framework for Natural Disaster Management in Malaysia . In Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-245-5, pages 406-411. DOI: 10.5220/0006367204060411


in Bibtex Style

@conference{iotbds17,
author={Mohammad Fikry Abdullah and Mardhiah Ibrahim and Harlisa Zulkifli},
title={Big Data Analytics Framework for Natural Disaster Management in Malaysia},
booktitle={Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2017},
pages={406-411},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006367204060411},
isbn={978-989-758-245-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - Big Data Analytics Framework for Natural Disaster Management in Malaysia
SN - 978-989-758-245-5
AU - Abdullah M.
AU - Ibrahim M.
AU - Zulkifli H.
PY - 2017
SP - 406
EP - 411
DO - 10.5220/0006367204060411