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Big Data Analytics and Preprocessing

  • 2021
  • OriginalPaper
  • Chapter
Published in:
Authors
Noha Shehab Corresponding author

Corresponding author Noha Shehab

  • Computers Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
Corresponding author noha.a.shehab@students.mans.edu.eg
Mahmoud Badawy

Mahmoud Badawy

  • Computers Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
Hesham Arafat

Hesham Arafat

  • Computers Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
Published in
Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges
Publisher
Springer International Publishing

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Abstract

Big data is a trending word in the industry and academia that represents the huge flood of collected data, this data is very complex in its nature. Big data as a term used to describe many concepts related to the data from technological and cultural meaning. In the big data community, big data analytics is used to discover the hidden patterns and values that give an accurate representation of the data. Big data preprocessing is considered an important step in the analysis process. It a key to the success of the analysis process in terms of analysis time, utilized resources percentage, storage, the efficiency of the analyzed data and the output gained information. Preprocessing data involves dealing with concepts like concept drift, data streams that are considered as significant challenges.

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previous chapter Rough Sets and Rule Induction from Indiscernibility Relations Based on Possible World Semantics in Incomplete Information Systems with Continuous Domains
next chapter Artificial Intelligence-Based Plant Diseases Classification
Title
Big Data Analytics and Preprocessing
Authors
Noha Shehab
Mahmoud Badawy
Hesham Arafat
Copyright Year
2021
Book
Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges

Print ISBN: 978-3-030-59337-7

Electronic ISBN: 978-3-030-59338-4

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-59338-4_2
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