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Multi-Objective Big Data View Materialization Using MOGA

Author

Listed:
  • Akshay Kumar

    (Jawaharlal Nehru University, India)

  • T. V. Vijay Kumar

    (Jawaharlal Nehru University, India)

Abstract

The COVID 19 Pandemic, has resulted in large scale of generation of Big data. This Big data is heterogeneous and includes the data of people infected with corona virus, the people who were in contact of infected person, demographics of infected person, data on corona testing, huge amount of GPS data of people location, and large number of unstructured data about prevention and treatment of COVID 19. Thus, the pandemic has resulted in producing several Zeta bytes of structured, semi-structured and unstructured data. The challenge is to process this Big data, which has the characteristics of very large volume, brisk rate of generation and modification and large data redundancy, in a time bound manner to take timely predictions and decisions. Materialization of views for Big data is one of the ways to enhance the efficiency of processing of the data. In this paper, Big data view selection problem is addressed, as a bi-objective optimization problem, using Multi-objective genetic algorithm.

Suggested Citation

  • Akshay Kumar & T. V. Vijay Kumar, 2022. "Multi-Objective Big Data View Materialization Using MOGA," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 13(1), pages 1-28, January.
    • Handle: RePEc:igg:jamc00:v:13:y:2022:i:1:p:1-28
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    References listed on IDEAS

    as
    1. T. V. Vijay Kumar & Biri Arun, 2017. "Materialized view selection using HBMO," 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. 8(1), pages 379-392, January.
    2. Biri Arun & T.V. Vijay Kumar, 2017. "Materialized View Selection using Artificial Bee Colony Optimization," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 13(1), pages 26-49, January.
    3. Biri Arun & T.V. Vijay Kumar, 2017. "Materialized View Selection Using Bumble Bee Mating Optimization," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 9(3), pages 1-27, July.
    4. Biri Arun & T.V. Vijay Kumar, 2015. "Materialized View Selection using Marriage in Honey Bees Optimization," International Journal of Natural Computing Research (IJNCR), IGI Global, vol. 5(3), pages 1-25, July.
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