Multi-Objective Big Data View Materialization Using MOGA

Akshay Kumar,T V Vijay Kumar

doi:10.4018/ijamc.292499

Akshay Kumar, T V Vijay Kumar

Open Access

https://doi.org/10.4018/ijamc.292499

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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.

Highlights

Information is one of the important criterion for the survival of Businesses in the present world
This paper addresses the bi-objective Big data view materialization problem using the Multi-Objective Genetic Algorithm (MOGA)
This paper proposed three objectives for Big data view materialization

Summary

Introduction

Information is one of the important criterion for the survival of Businesses in the present world. Big data applications are required to process large amounts of data, which is cleaned, integrated, and presented in different forms, for making optimal business decisions. Big data is generated from a variety of data sources, which generally produces inconsistent data at different rates, leading to complex and challenging data cleaning and integration processes. Big data in its raw form is not suitable for business decisions, rather it is processed to create useful information for the benefit of an organization. This is referred to as the value of Big data. Validity, vulnerability and volatility are other important considerations of Big data processing (Khan et al, 2014; Gandomi et al, 2015; Firican, 2017)

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