Big Data Analytics Performance Enhancement For Covid-19 Data Using Machine Learning And Cloud

Abstract

The exponential rise in software computing, internet and web-services has broadened the horizon for BigData that demands robust and highly efficient analytics system to serve timely and accurate distributed data support. The distributed frameworks with parallelized computing have been found key driving force behind the contemporary BigData analytics systems; however, the lack of optimal data pre-processing, feature sensitive computation and more importantly feature learning makes major at-hand solutions inferior, especially in terms of time and accuracy. Unlike major at hand methods employing machine learning for BigData analytics, in this paper the key emphasis was made on improving pre-processing, low-dimensional semantic feature extraction and lightweight improved machine learning based feature learning for BigData analytics. Noticeably, the proposed model hypothesizes that an analytics solution with BigData characteristics must have the potential to process humongous, heterogenous, unstructured and multi-dimensional features to yield time-efficient and accuracy analytical outputs. In this reference, we proposed a state-of-art new and robust BigData analytics model, specially designed for Spark distributed framework. To process analytical task our proposed model at first employs tokenization, followed by Word2Vec based semantic feature extraction using CBOW and N-Skip-Gram methods. Our proposed model was found more effective with Skip-Gram Word2Vec feature extraction. Simulation results with a publicly available COVID-19 data exhibited better performance than existing K-Means based MapReduce distributed data frameworks.