Big Data and Analytics

Big Data and Analytics

Sheik Abdullah A. (Thiagarajar College of Engineering, India) and Priyadharshini P. (Thiagarajar College of Engineering, India)
Copyright: © 2020 |Pages: 19
DOI: 10.4018/978-1-5225-9750-6.ch003

Abstract

The term Big Data corresponds to a large dataset which is available in different forms of occurrence. In recent years, most of the organizations generate vast amounts of data in different forms which makes the context of volume, variety, velocity, and veracity. Big Data on the volume aspect is based on data set maintenance. The data volume goes to processing usual a database but cannot be handled by a traditional database. Big Data is stored among structured, unstructured, and semi-structured data. Big Data is used for programming, data warehousing, computational frameworks, quantitative aptitude and statistics, and business knowledge. Upon considering the analytics in the Big Data sector, predictive analytics and social media analytics are widely used for determining the pattern or trend which is about to happen. This chapter mainly deals with the tools and techniques that corresponds to big data analytics of various applications.
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Big Data Application

The Internet of things (IOTs) is a network-connected object. The IOTs model contains communication devices, which are embedded sensor devices in real world. These are interconnected with Sensor devices and objects, which are used to communication technologies. In these technologies are: RFID, Bluetooth, Wi-Fi and GSM. An IOT object is also able to collect and transfer sensitive data. With this technology, data transfer over the internet does not need human to human or human to computer interaction. The main critical component in the IoTs process is data. Data transmission from communication devices to remotely control devices (Abirami et al., 2018) is focused on the volume aspect based on a lot of data set maintenance.

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