Friday, 15 June 2018

Exploring new datatypes in Neo4j 3.4 and the Open Beer Database - part 2/2

In the previous blogpost I imported the Open Beer Database into Neo4j and added some new fancy spatial data to it. Now in this post I would like to explore that data. As a reminder, you can find the full
Let's take a look.

First we will just look at the basic OpenBeerDB data. The schema is quite straightforward:

Thursday, 14 June 2018

Exploring new datatypes in Neo4j 3.4 and the Open Beer Database - part 1/2

Recently, I gave a talk at the Amsterdam, Brussels and London Neo4j meetups about some of the new and exciting features in Neo4j 3.4. While preparing for it, I was looking for material and I found some very cool stuff that powerfully explains the new features. The best resource is probably this post by Ryan Boyd, and the video that goes with it:



Ryan does a great job at explaining the new features, and goes into some detail on the new temporal and spatial data types that you can now use in Neo4j 3.4. You can explore these new features yourself by accessing the Neo4j Sandbox developed specifically for this purpose. Or you can just do what I did, and use the Neo4j Desktop to spin up a Neo4j instance, and access the "guide". You do that by typing
:play https://guides.neo4j.com/sandbox/3.4/index.html
into the Neo4j browser, and then you can access the entire guide, add some data to your dataset, and play around.

Friday, 8 June 2018

Podcast interview with Jeffrey Miller, ICC

Here's a podcast episode that I have been wanting/needing to publish for a long time .Jeffrey A. Miller works as a Senior Consultant in Columbus, Ohio as a consultant in effective software development practices with lots of organisations. Jeffrey has delivered presentations at regional technical conferences and user groups on topics including Neo4j technology, knowledge management, and humanitarian healthcare projects - and that of course became a great setup for our conversation.


Also - I found this really interesting: Jeffrey and his wife, Brandy, are aspiring adoptive parents and have written a fun children’s book called “Skeeters” with proceeds supporting adoption. Learn more about the project at http://skeeterbooks.com/.

Friday, 1 June 2018

Using Neo4j Bloom for fraud detection, discovery and visualization

Over the past couple of weeks, I have been discussing and showing Neo4j's new Bloom graph discovery and visualization product to everyone that would have a moment to spare. It's soooo much fun to show a tool you love, and Bloom is definitely one of those. And I have also recorded some odf these demo-sessions - you can find part 1part 2 and part 3 of these recordings on this blog. All of these recordings use my (in)famous Belgian Beergraph dataset - and that's all good fun...

But of course, exploring a beergraph is not really a "business-y" use case. So I decided I would record a Bloom demo using a realistic dataset that centers around using Neo4j for Fraud Detection purposes. You will find all of the important concepts of the Beergraph demos here as well:
  • navigating the graph using graph patterns
  • using nifty selection / deselection techniques to only show what you need in the graph
  • creating better visualizations with colours and icons
  • editing the graph straight from the Bloom interface
  • creating custom search phrases for business uses and giving them near-natural language graph search capabilities.
Here's the recording: