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Mastering Concurrency Programming with Java 9, Second Edition

You're reading from   Mastering Concurrency Programming with Java 9, Second Edition Fast, reactive and parallel application development

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781785887949
Length 516 pages
Edition 2nd Edition
Languages
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Author (1):
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Javier Fernández González Javier Fernández González
Author Profile Icon Javier Fernández González
Javier Fernández González
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Table of Contents (14) Chapters Close

Preface 1. The First Step - Concurrency Design Principles FREE CHAPTER 2. Working with Basic Elements - Threads and Runnables 3. Managing Lots of Threads - Executors 4. Getting the Most from Executors 5. Getting Data from Tasks - The Callable and Future Interfaces 6. Running Tasks Divided into Phases - The Phaser Class 7. Optimizing Divide and Conquer Solutions - The Fork/Join Framework 8. Processing Massive Datasets with Parallel Streams - The Map and Reduce Model 9. Processing Massive Datasets with Parallel Streams - The Map and Collect Model 10. Asynchronous Stream Processing - Reactive Streams 11. Diving into Concurrent Data Structures and Synchronization Utilities 12. Testing and Monitoring Concurrent Applications 13. Concurrency in JVM - Clojure and Groovy with the Gpars Library and Scala

First example - a keyword extraction algorithm

In this section, you are going to use a phaser to implement a keyword extraction algorithm. The main purpose of these kinds of algorithms is to extract the words from a text document or a collection of documents, which define the document or the document inside the collection, better. These terms can be used to summarize the documents, cluster them, or to improve the information search process.

The most basic algorithm to extract the keywords of the documents in a collection (but it's still commonly used nowadays) is based on the TF-IDF measure where:

  • Term Frequency (TF) is the number of times that a d appears in a document.
  • Document Frequency (DF) is the number of documents that contain a word. The Inverse Document Frequency (IDF) measures the information that word provides to distinguish a document from others. If a word is...
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