An Exploratory analysis of Machine Learning adaptability in Big Data Analytics Environments: A Data Aggregation in the age of Big Data and the Internet of Things

Abstract

The paper discusses a new concept combining the potentialities of Big Data processing as well as machine learning developed for security monitoring of mobile Internet of Things. The structure of the security monitoring system is considered as a most effective and useful element to create a new viewpoint of mobile IoT. This article focuses implementation of machine learning in online education. Thus mobile IoT has found successful application in few areas such as security monitoring in public places, transport management, medicine, smart houses, industrial production, electrical consumption, and robotics. All the mathematical foundations along with issues related to this have been considered in this study. In order to solve the classification task, several machine learning mechanisms have been mentioned in this paper. Large organizations are incorporating data-driven actions, and decision making in organizational function. The role of data aggregation is effective here achieving the business objectives. Vast amount of raw data can be processed linearly through data aggregation. This article describes the interaction of data aggregation through wireless networking assuming its effectiveness in online education. Data aggregation in machine learning is highlighted based on evidence based data. The purpose of this research article is to investigate the machine learning adaptability in big data analytics environments with the approach of IoT. In order to collect accurate data, the researcher has taken the help of a secondary data collection method. It has helped the researcher to find out the valid information about mobile IoT. In addition, qualitative methods have been adapted to malaise the collected data within a systematic way. Moreover, this study will help the readers to understand the value of mobile IoT helping in machine learning adaptability in big data analytics.