The text establishes a clear correlation between the challenges presented by the characteristics of big data (velocity, volume, and variety) and the imperative to employ sophisticated analysis techniques.
Published in Chapter:
Big Data Analysis Techniques: Data Preprocessing Techniques, Data Mining Techniques, Machine Learning Algorithm, Visualization
Pranali Dhawas (G.H. Raisoni College of Engineering, Nagpur, India),
Minakshi Ashok Ramteke (G.H. Raisoni College of Engineering, Nagpur, India), Aarti Thakur (G.H. Raisoni College of Engineering, Nagpur, India),
Poonam Vijay Polshetwar (CSMSS College of Polytechnic, India),
Ramadevi Vitthal Salunkhe (Rajarambapu Institute of Technology, India), and
Dhananjay Bhagat (G.H. Raisoni College of Engineering, Nagpur, India)
Copyright: © 2024
|Pages: 26
DOI: 10.4018/979-8-3693-0413-6.ch007
Abstract
Big data analysis techniques are the methods and tools utilized for extracting insights and knowledge from vast and intricate datasets. Due to the increasing velocity, volume, and variety of data being produced, conventional data analysis methods have become inadequate. Therefore, big data analysis techniques employ advanced computational and statistical methods to extract treasured information from big data. There are several big data analysis techniques, including data mining, natural language processing, machine learning, predictive analytics, and deep learning. For example, data mining involves identifying patterns and relationships within data sets, while machine learning enables systems to learn from data without explicit programming. Additionally, natural language processing focuses on analyzing human language, and predictive analytics utilizes statistical modeling techniques to predict future outcomes. Deep learning, which uses neural networks to model complex data patterns, is also a common big data analysis technique.