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Flow data processing paradigm and its application in smart city using a cluster analysis approach

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Abstract

In the digital revolution era the work style of the people changed into real and live communicative systems. Smart cities are evolved from the smart ecosystem with various domains like healthcare, information exchange, transportation, buildings etc., the presence of multiple information system makes these smart cities into a heterogeneous which includes multiple data transfer based on the large no of interconnected sub systems. Since the systems provide data from various terminals to the host the amount of information generates is same as the chances of increasing the challenges and issues in the smart city. These flow data from various sources are handled using cluster computing and this proposed research provides the data flow in the smart city using cluster computing. Apache spark is the tool used to handle the issues in the smart city which is much better than the Hadoop. The results discuss the comparison between the applications in same environment on both the tools.

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Acknowledgements

The paper is supported by the National Natural Science Foundation Project “Research on key technology of malicious node identification in wireless sensor network”. 61772101, 2018.1-2021.12 and Liaoning Provincial Education Department Project, the Innovation Ream of Liaoning University, “ubiquitous network security and computing technology” ZX2015KJ018.

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Authors and Affiliations

  1. Software Center, Northeastern University, Shenyang, People’s Republic of China

    Xiang Zou & Tao Wen

  2. Dalian Neusoft Institute of Information, Dalian, People’s Republic of China

    Xiang Zou, Jinghua Cao, Wei Sun, Quan Guo & Tao Wen

  3. Dalian Maritime University, Dalian, People’s Republic of China

    Jinghua Cao

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  1. Xiang Zou
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  2. Jinghua Cao
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  3. Wei Sun
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  4. Quan Guo
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  5. Tao Wen
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Correspondence to Tao Wen.

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Zou, X., Cao, J., Sun, W. et al. Flow data processing paradigm and its application in smart city using a cluster analysis approach. Cluster Comput 22, 435–444 (2019). https://doi.org/10.1007/s10586-018-2839-y

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  • DOI: https://doi.org/10.1007/s10586-018-2839-y

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