In part one, we learned to listen to our users, in part two we began learning how to talk back. Before we go any further in to the stored procedure, how about we build a little front end to show off the work we’ve done so far on this proof of concept? That will also make things easier to test out and let us get into the mindset of the user. There are a ton of options here, lots of folks like Spring and Spring Boot. Others are more hipsters and into Micronaut. I am even more of a hipster and prefer to use Jooby, but it doesn’t really matter. We’ll be using Cypher, the Neo4j Drivers and the Stored Procedures we build along the way so technically you can do this in just about any language. Continue reading →
In part one our this building a chatbot series, we figured out how to use OpenNLP to “hear” what a user is saying and figure out both their intent and any entities they may have mentioned. Today we’re going to learn how to use Neo4j to talk back… like an impudent child.
Last year eBay built a chatbot using Neo4j. Unfortunately we have grown so big I didn’t get a chance to work on that project and kinda feel left out. So I decided I’m going to build my own chatbot with Neo4j. As usual I’ve never done this before, have very little idea what I’m doing, have no team, and have barely any time to get this done. So with those disclaimers out of the way, let’s see what we can do. Continue reading →
Is not that thing above. That’s a Chart, not a Graph. But anyway…Neo4j is designed to support the property graph model natively. There are a host of other technologies that can bolt-on a “graph layer” of some kind. However it doesn’t make them a graph database. It’s like adding a rear spoiler to a van, sure it may look cool… or ridiculous, but it won’t make it a race car. Don’t fall for it. If you need fast graph queries, use a real graph database. But today we won’t talk about that. Instead we’re going to talk about the real property graph… Continue reading →
In the last blog post, we saw how we can use Neo4j to find the merchants where credit card fraud originated or was used for testing stolen data in order to prevent further fraudulent charges. It stemmed from a webinar on our amazing youtube channel with has hundreds of videos about graphs and Neo4j. We will continue diving in to the technical details by looking at how Neo4j can help you find Fraud Rings. The way this fraud works is that a large set of synthetic accounts are created and act like normal customers. Over time they request higher and higher levels of credit which they pay back on time. Then they all request the maximum credit they can get, take out the money, and disappear! Let’s find them before this happens.
It’s no secret that one of our hottest use cases lately has been Fraud Detection. A while back we did a webinar talking about some of the ways you could use Neo4j to fight fraud. Watch it, if you haven’t yet. Today I want to augment that webinar with some cypher queries. Let’s see how it works: Continue reading →
The Awesome Procedures on Cypher (APOC) plugin is packed with goodies to make your life working with Neo4j easier. Sometimes you want to use these goodies in your own stored procedures…and you want to be able to test them. So how do we go about doing that exactly? Continue reading →
How does the saying go? There are lies, damned lies, and benchmarks. I’ve already made my feelings about database vendor benchmarks known, but in case you missed it. They are complete fabrications. Never to be trusted, never ever. Never. But vendors love to do benchmarks, they love spreading fear, uncertainty and doubt instead of spending their time doing productive things like creating useful content that teaches people how to use their product. I wish I could just ignore this nonsense and focus on what really matters, like helping our customers to successful production rollouts, but alas, here we are.
If you are friends with Jessie, and Jessie is friends with Amy, there is a good chance you’ll eventually become friends with Amy too. In terms of a graph, this would be like a graph with three nodes and two relationships eventually building a third relationship to form a clique. This simple concept is one of the basis for recommendation engines. There are fancy terms for it, like “triadic closure” but basically it just means we are making triangles. But what about Amy’s friend Delilah? Is there a good chance now that you are friends with Amy that you’ll become friends with her? What about Jessie and Delilah? Can we extend the pattern to four nodes or five nodes and go beyond our simple triangle? Continue reading →