Application of Parameter Estimation: A Big Data Analytics Perspective

Abstract

At the present time there are various ways to human interaction like human to machine and machine to machine interaction. Today the medium of human interaction is social media which is very popular. The social media interaction can be a sharing ideas, sending text messages, video, and audio call and machine to machine interaction such as automatic door opening, traffic control, telemedicine, weather forecast etc. the machine learning are using different devices for a particular purpose. Those devices are generating a huge amount of data in a various format like numerical, text, audio, video, images in every second such types of data is known as big data. Big data can be defined based on three parameters, volume, variety, and velocity. If the data has volume (large data sets), variety (text, images, audio, video) and velocity (speed of data). For the store and process those complex data we need a reliable and appropriate algorithm. In this paper, we have proposed a framework based on big data parameter estimation. The proposed framework will predict the useful information from the heterogeneous with huge amount of data sets through suitable sampling techniques.