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Completed on 2017-04-24
About the Book
This book is based on the author's experience as a developer and consultant and consists of seven chapters:
- Network programming techniques for the Internet of Things (IoT)
- Natural Language Processing using OpenNLP including using existing models and creating your own models
- Machine learning using the Spark mllib library (document custering, logistic regression, word2vec similarity)
- Anomaly detection machine learning example
- Web scraping and information gathering
- Using rich semantic and linked data sources on the web to enrich the data models you use in your applications
- Java Strategies for Knowledge Management using local and cloud data
The first chapter on IoT is a tutorial on network programming techniques for IoT development. I have also used these same techniques for multiplayer game development and distributed virtual reality systems. This chapter stands on its own and is not connected to any other material in this book. To be clear, this chapter covers some of the network programming techniques you will need for IoT applications and does not cover development using IoT devices.
The second chapter shows you how to use the OpenNLP library to use machine learning to train your own maximum entropy classifiers and to segment sentences, tag parts of speech, and generally process English language text. Both this chapter and the next chapter on machine learning using the Spark MLlib library use machine learning techniques. The Spark MLlib is convenient to use for development on your laptop and you can use the same code you develop on Spark clusters to get near real time processing of big data.
The last two chapters are for information architects or developers who would like to develop information design and knowledge management skills. I stress the idea of leveraging both cloud data (e.g., Microsoft Office 365 and Google Drive) and local data sources. In order to simplify the final example program in the book, I use Google Takeout to export my data (Microsoft Word and Excel file formats, mailbox, and iCal calendar files). It is left as a project for the reader to extend the example program to interface with the cloud data sources their organization uses.
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