Book Image

Learning Neo4j 3.x - Second Edition

By : Jerome Baton
Book Image

Learning Neo4j 3.x - Second Edition

By: Jerome Baton

Overview of this book

Neo4j is a graph database that allows traversing huge amounts of data with ease. This book aims at quickly getting you started with the popular graph database Neo4j. Starting with a brief introduction to graph theory, this book will show you the advantages of using graph databases along with data modeling techniques for graph databases. You'll gain practical hands-on experience with commonly used and lesser known features for updating graph store with Neo4j's Cypher query language. Furthermore, you'll also learn to create awesome procedures using APOC and extend Neo4j's functionality, enabling integration, algorithmic analysis, and other advanced spatial operation capabilities on data. Through the course of the book you will come across implementation examples on the latest updates in Neo4j, such as in-graph indexes, scaling, performance improvements, visualization, data refactoring techniques, security enhancements, and much more. By the end of the book, you'll have gained the skills to design and implement modern spatial applications, from graphing data to unraveling business capabilities with the help of real-world use cases.
Table of Contents (24 chapters)
Title Page
Credits
About the Authors
Acknowledgement
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Questions and answers


Q1: In order to build a recommendation system, I need an artificial intelligence engine that will take a look at my data and discover the recommendation patterns for me automatically.

  1. True
  2. False

Answer: False. Recommender systems can be based on business knowledge that your staff already has, a visual pattern you discover while browsing the data, or some kind of algorithmic machine learning process. All three can provide meaningful recommendation patterns for your business applications.

Q2: Recommender systems can only be applied in an Amazon-style retail environment, where you have a massive amount of data to base your recommendations on.

  1. True
  2. False

Answer: False. Recommendations are useful in many different business domains, not just retail product recommendations. Fraud detection systems (I recommend that you put this person in jail.) are just one example of a business application that has nothing to do with retail but that will use the same pattern matching capabilities...