Book Image

Java Data Analysis

By : John R. Hubbard
Book Image

Java Data Analysis

By: John R. Hubbard

Overview of this book

Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the aim of discovering useful information. Java is one of the most popular languages to perform your data analysis tasks. This book will help you learn the tools and techniques in Java to conduct data analysis without any hassle. After getting a quick overview of what data science is and the steps involved in the process, you’ll learn the statistical data analysis techniques and implement them using the popular Java APIs and libraries. Through practical examples, you will also learn the machine learning concepts such as classification and regression. In the process, you’ll familiarize yourself with tools such as Rapidminer and WEKA and see how these Java-based tools can be used effectively for analysis. You will also learn how to analyze text and other types of multimedia. Learn to work with relational, NoSQL, and time-series data. This book will also show you how you can utilize different Java-based libraries to create insightful and easy to understand plots and graphs. By the end of this book, you will have a solid understanding of the various data analysis techniques, and how to implement them using Java.
Table of Contents (20 chapters)
Java Data Analysis
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

MapReduce in MongoDB


MongoDB implements the MapReduce framework with its mapReduce() command. An example is shown in Figure 11-6.

Figure 11-6. Running MapReduce in MongoDB

The first two statements define the JavaScript functions map1() and reduce1(). The third statement runs MapReduce on our library.books collection (see Chapter 10, NoSQL Databases), applying those two functions, and naming the resulting collection "map_reduce_example". Appending the find() command causes the output to be displayed.

The map1() function emits the key-value pair (p, 1), where p is the books.publisher field. So this will generate 19 pairs, one for each books document. For example, one of them will be ("OXF", 1). In fact, four of them will be ("MHE", 1), because there are four documents in the books collection whose publisher field is "MHE".

The reduce1() function uses the Array.sum() method to return the sum of the values of the second argument (numBooks) for each value of the first argument (pupId). For example...