The rise of machine learning for big data analytics

Abstract

This paper addresses the rise of machine learning for big data analytics. First, machine learning and several terms related to machine learning are defined and explained in details and these terms include artificial intelligence, data mining, data science, data analytics and knowledge discovery, statistics and Business Intelligence. These definitions will show how these terms are inter-related to each other. Then, the definition of big data is outlined based on three terms: Volume, Velocity and Variety. Implementing a good big data strategy is very crucial in order to guarantee the success of applying machine learning for learning big data. As a result, the trending in Big Data is also illustrated and defined based on the landscape of big data; Infrastructure, Analytics, Applications, Cross-Infrastructures/Analytics, Open Sources, Data Sources and API, Incubators and Schools. This paper also addresses some of the open source facilities that are available for public in order to ensure that large scale of machine learning application can be realized. Finally, in conclusion, the big trend over the last few months in Big Data analytics has been the increasing focus on artificial intelligence to help analyze massive amounts of data and derive predictive insights. AI/machine learning is now precipitating a trend towards the emergence of the application layer of Big Data. The combination of Big Data and AI will drive incredible innovation across pretty much every industry. From that perspective, the Big Data opportunity is probably even bigger than people thought.