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Java Data Analysis

You're reading from   Java Data Analysis Data mining, big data analysis, NoSQL, and data visualization

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787285651
Length 412 pages
Edition 1st Edition
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Author (1):
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John R. Hubbard John R. Hubbard
Author Profile Icon John R. Hubbard
John R. Hubbard
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Table of Contents (14) Chapters Close

Preface 1. Introduction to Data Analysis FREE CHAPTER 2. Data Preprocessing 3. Data Visualization 4. Statistics 5. Relational Databases 6. Regression Analysis 7. Classification Analysis 8. Cluster Analysis 9. Recommender Systems 10. NoSQL Databases 11. Big Data Analysis with Java A. Java Tools Index

Some examples of MapReduce applications


Here are a few examples of big data problems that can be solved with the MapReduce framework:

  1. Given a repository of text files, find the frequency of each word. This is called the WordCount problem.

  2. Given a repository of text files, find the number of words of each word length.

  3. Given two matrices in sparse matrix format, compute their product.

  4. Factor a matrix given in sparse matrix format.

  5. Given a symmetric graph whose nodes represent people and edges represent friendship, compile a list of common friends.

  6. Given a symmetric graph whose nodes represent people and edges represent friendship, compute the average number of friends by age.

  7. Given a repository of weather records, find the annual global minima and maxima by year.

  8. Sort a large list. Note that in most implementations of the MapReduce framework, this problem is trivial, because the framework automatically sorts the output from the map() function.

  9. Reverse a graph.

  10. Find a minimal spanning tree (MST) of a...

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