Database Reference
In-Depth Information
Chapter 5
Big Data Analysis
Abstract
In this chapter, we introduce the methods, architectures and tools for
big data analysis. The analysis of big data mainly involves analytical methods
for traditional data and big data, analytical architecture for big data, and software
used for mining and analysis of big data. Data analysis is the final and the most
important phase in the value chain of big data, with the purpose of extracting useful
values, providing suggestions or decisions. Different levels of potential values can
be generated through the analysis of datasets in different fields.
5.1
Traditional Data Analysis
Traditional data analysis means to use proper statistical methods to analyze massive
first-hand data and second-hand data, to concentrate, extract, and refine useful data
hidden in a batch of chaotic data, and to identify the inherent law of the subject
matter, so as to develop functions of data to the greatest extent and maximize the
value of data. Data analysis plays a huge guidance role in making development plans
for a country, as well as understanding customer demands and predicting market
trend by enterprises.
Big data analysis can be deemed as the analysis of a special kind of data.
Therefore, many traditional data analysis methods may still be utilized for big data
analysis. Several representative traditional data analysis methods are examined in
the following, many of which are from statistics and computer science.
Cluster Analysis
: cluster analysis is a statistical method for grouping objects,
and specifically, classifying objects according to some features. Cluster analysis
is used to differentiate objects with certain features and divide them into some
categories (clusters) according to these features,such that objects in the category
will have high homogeneity different categories will have high heterogeneity.
Cluster analysis is an unsupervised study method without the use of training
data.
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