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Study and analysis of big data for characterization of user association in large scale

Author

Listed:
  • Wei-wei Zhang

    (Xian University of Posts and Telecommunication)

  • Jyoti Bhola

    (National Institute of Technology)

  • Rajeev Kumar

    (Chitkara University)

  • Nitin Saluja

    (Chitkara University)

Abstract

The volume and the data detail are increasing like social media, multimedia and internet of things produced huge data flow in structured and unstructured format. The academia, government, and industry have the great attention for data generation. The cloud computing is conjoined with the data and it provides the user ability to utilize the commodity computing for queries process through the several datasets and timely return of resultant set. The several serious challenges are produced by the amount of collected data such as transfer speed and security issues. Big Data is in its initial stage and it required to be classifies the various attributes of big data such as management and quick progression rate. The results show that the one big executer configuration performance is better as compared to the six small executer configuration. It is also observed that the more executer configuration increase the utilization of the resources and the high probability is led by the resource contention so there is negative impact on the system performance. The fewer amounts of data are produced and it performs better for other applications. The data size is not always reduced by enabling data compression. The data size is reduced up-to 72% by compression and memory serialization.

Suggested Citation

  • Wei-wei Zhang & Jyoti Bhola & Rajeev Kumar & Nitin Saluja, 2022. "Study and analysis of big data for characterization of user association in large scale," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 375-384, March.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:1:d:10.1007_s13198-021-01434-y
    DOI: 10.1007/s13198-021-01434-y
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    References listed on IDEAS

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