Big Data Collection, Filtering, and Extraction of Features

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Author(s): Ganesh B. Regulwar (Vardhaman College of Engineering, India), Ashish Mahalle (G.H. Raisoni College of Engineering, Nagpur, India), Raju Pawar (G.H. Raisoni College of Engineering, Nagpur, India), Swati K. Shamkuwar (G.H. Raisoni College of Engineering, Nagpur, India), Priti Roshan Kakde (G.H. Raisoni College of Engineering, Nagpur, India)and Swati Tiwari (G.H. Raisoni College of Engineering, Nagpur, India)
Copyright: 2024
Pages: 23
Source title: Big Data Analytics Techniques for Market Intelligence
Source Author(s)/Editor(s): Dina Darwish (Ahram Canadian University, Egypt)
DOI: 10.4018/979-8-3693-0413-6.ch005

Keywords: Business & Management / Business Intelligence / Data Analysis and Statistics / Engineering Science Reference

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Abstract

Big data is a term used to describe data sets that are too large or intricate for traditional data processing systems to handle. Big data collection, filtering, and feature extraction are significant procedures in data science that enable organizations to scrutinize vast amounts of data to obtain insights and make well-informed decisions. Following filtration, feature extraction is executed to identify vital patterns and relationships in the data using techniques such as clustering, principal component analysis, and association rule mining. The primary objective of big data collection, filtering, and feature extraction is to identify valuable information that can aid in decision-making, enhance operations, and develop new products and services. These processes are essential for organizations that aspire to remain competitive and at the forefront of the constantly changing data landscape.

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