Performance Evaluation Of Application Specific Big Data Systems With Multi-domain Big Data Framework Using Machine Learning

The world is getting adopted towards Big Data analytics and related tools which has increased demand for secure, reliable and efficient technology for processing large amount of data flowing on internet. The analytics of datasets has provided opportunities in industries and academics to look into the drifts while creating these large datasets. The developments in network technology and other data analytics systems has increased the data collection in various domains i.e. social networking, medical care, agriculture and horticulture, business and finance, educational industries and smart cities. The use of such amount of data through process of analytics using advanced techniques generally helps in predicting the trends in future and reviewing the change with optimal precision. The diverse nature of data, quantity and speed of creation is very much high with obsolete security procedures which make it un-reliable to use. These provide scalable and efficient results as compared to the traditional platforms and techniques. The research already done focusses on independent domain specific ICT applications. But less work has been done in making a collective approach to deal with growing use of data, systematic storage, extraction of domain related datasets, security and analytics thereof. The research presented evaluates the big data platforms with novel muti-domain big data framework using the available techniques for security and other challenges.