Mining Data Streams

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Author(s): Prasanna Lakshmi Kompalli (Gokaraju Rangaraju Institute of Engineering and Technology, India)
Copyright: 2019
Pages: 28
Source title: Sentiment Analysis and Knowledge Discovery in Contemporary Business
Source Author(s)/Editor(s): Dharmendra Singh Rajput (VIT University, India), Ramjeevan Singh Thakur (Maulana Azad National Institute of Technology, India)and S. Muzamil Basha (VIT University, India)
DOI: 10.4018/978-1-5225-4999-4.ch014

Keywords: Business and Organizational Research / Business Science Reference / Knowledge Discovery / Library & Information Science

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Abstract

In recent years, advancement in technologies has made it possible for most of the present-day organizations to store and record large streams of data. Such data sets which continuously and rapidly grow over time are referred to as data streams. Mining of such data streams is a unique opportunity and also a challenging task. Data stream mining is a process of gaining knowledge from continuous and rapid records of data. Due to increased streaming information, data stream mining has attracted the research community in the recent past. There is voluminous of literature which has been published in this domain over the past few years. Due to this, isolating the correct literature would be a grueling task for researchers and practitioners. While addressing a real-world problem, it would be more difficult to find relevant information as it would be hidden in data streams. This chapter tries to provide solution as it would be an amalgamation of all techniques used for data stream mining.

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