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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 with an infected person, demographics of infected persons, data on corona testing, a huge amount of GPS data of people location, and a large amount of unstructured data about prevention and treatment of  COVID-19. Thus, the pandemic has resulted in producing several Zettabytes 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.

Multi-Objective Big Data View Materialization Using MOGA