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Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

Author

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  • Gunasekaran Manogaran

    (School of Information Technology and Engineering, VIT University, Vellore, India)

  • Daphne Lopez

    (School of Information Technology and Engineering, VIT University, Vellore, India)

Abstract

Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.

Suggested Citation

  • Gunasekaran Manogaran & Daphne Lopez, 2017. "Disease Surveillance System for Big Climate Data Processing and Dengue Transmission," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 8(2), pages 88-105, April.
    • Handle: RePEc:igg:jaci00:v:8:y:2017:i:2:p:88-105
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    Cited by:

    1. Yasmine Lamari & Said Chah Slaoui, 2018. "PDC-Transitive: An Enhanced Heuristic for Document Clustering Based on Relational Analysis Approach and Iterative MapReduce," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 1-18, June.

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