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Introduction to Big Data Read chapter Read first chapter

2016 | OriginalPaper | Chapter

2. Big Data Analytics

Authors : Chun-Wei Tsai, Chin-Feng Lai, Han-Chieh Chao, Athanasios V. Vasilakos

Published in: Big Data Technologies and Applications

Publisher: Springer International Publishing

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Abstract

The age of big data is now coming. But the traditional data analytics may not be able to handle such large quantities of data. The question that arises now is, how to develop a high performance platform to efficiently analyze big data and how to design an appropriate mining algorithm to find the useful things from big data. To deeply discuss this issue, this paper begins with a brief introduction to data analytics, followed by the discussions of big data analytics. Some important open issues and further research directions will also be presented for the next step of big data analytics.

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Footnotes
1
In this chapter, by the data analytics, we mean the whole KDD process, while by the data analysis, we mean the part of data analytics that is aimed at finding the hidden information in the data, such as data mining.
 
2
In this chapter, by an unlabeled input data, we mean that it is unknown to which group the input data belongs. If all the input data are unlabeled, it means that the distribution of the input data is unknown.
 
3
In this paper, the analysis framework refers to the whole system, from raw data gathering, data reformat, data analysis, all the way to knowledge representation.
 
4
The whole system may be down when the master machine crashed for a system that has only one master.
 
5
The learner typically represented the classification function which will create the classifier to help us classify the unknown input data.
 
6
The basic idea of [128] is that each ant will pick up and drop data items in terms of the similarity of its local neighbors.
 
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Metadata
Title
Big Data Analytics
Authors
Chun-Wei Tsai
Chin-Feng Lai
Han-Chieh Chao
Athanasios V. Vasilakos
Copyright Year
2016
Book
Big Data Technologies and Applications

Print ISBN: 978-3-319-44548-9

Electronic ISBN: 978-3-319-44550-2

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-44550-2_2

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