In the last post, we created a User model, built the login and registration pages, hooked everything up in our front end framework Jooby and got the ball rolling. I’m no designer so I am borrowing a Application Bootstrap Theme and tweaking that as we go along (if you are a designer, pull requests are welcomed). At this stage a ton of it is just mockup, but we will replace it with real functionality. This is what we have so far:
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We started our back end service in the last blog post and created a schema and the ability to create and fetch users. We are going to flip to the front end to make use of these abilities and work on both side by side. The goal this time is to be able to register and sign in a user. If you are a regular reader you know I’m a fan of the Jooby framework, so we’re going to use that again. After creating a shell application, what I want to do is to be able to connect to the API we’re building, so we’ll be using Retrofit to turn our HTTP API into a Java interface.
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We came up with an idea for a dating site and an initial model in Part One. Next we are going to work on a back end HTTP API, because I’m old school and that’s the way I like it. We will build our HTTP API right into Neo4j using an extension which turns Neo4j from a Server into a Service. Unlike last time where we wrote a clone of Twitter, I don’t really know where I’m going with this, so let’s start with some of the obvious API endpoints and then we can design and build more as we go along. Is this Agile or am I just being an idiot? I can’t tell, so onward we go.
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You might have already heard that Facebook is getting into the Dating business. Other dating sites have been using graphs in the past and we’ve looked at finding love using the graph before. It has been a while though, so let’s return to the topic making use of the new Date and Geospatial capabilities of Neo4j 3.4. I have to warn you though that I’ve been with Helene for almost 15 years and missed out on all this dating site fun, what I do know I blame Colin for it and some pointers from the comments section of this blog post.
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Alchemy is a philosophical and protoscientific tradition practiced throughout Europe, Africa and Asia. Its aim is to purify, mature, and perfect certain objects. In popular culture we often see the case of shadowy figures trying to turn lead into gold to make themselves immensely rich or to ruin the world economy. In our case we will not be transmuting lead into gold, but documents into graphs which is just as good. In the past, we had used “Alchemy API” but they were purchased by IBM and retired. You can get similar functionality with IBM Watson, but let’s do something else instead. Let’s add Entity Extraction right into Neo4j.
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If you have started or are thinking about starting a Graph project, you ought to get in touch with me. I’ve been involved in hundreds of graph database backed projects and chances are I can point you in the right direction. It doesn’t cost anything to get on a goto meeting for an hour and talk about it. Contact me at max@neo4j.com to schedule it. If you are very serious and have a little budget allocated then I recommend you sign up for one of our bootcamps. You’ll be amazed at what we can accomplish together in a very short time. I’ll even make you a deal, if you sign up for a bootcamp and end up buying a commercial license, we’ll give you a week of professional services absolutely free.
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One of the symptoms of any fast growing company is the lack of available meeting rooms. The average office worker gets immense satisfaction to their otherwise mundane workday when they get to kick someone else out of the meeting room they booked. Of course that joy can be cut short (along with their career) once realizing some unnoticed VIP was unceremoniously kicked out. It’s not a super exciting use case, but today I’m going to show you how to use Neo4j to perform some scheduling gymnastics.
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When I was growing up, the Neo Geo was the high end gaming system around. It was however prohibitively expensive for most people… and definitely out of my price range. I grew up in a mobile home park in Union City, CA. Near the old drive in theater now long gone. It was also next to an industrial park in Hayward where a food truck would make the best burritos $3 could buy. A search for the best burritos in Union City would have missed this food truck gem. Geographic boundaries can be a problem when searching for things by specific places. To get around this problem, we tend to use latitude and longitude and then perform a radius or bounding box search. Today I want to present to you a hybrid approach using Neo4j.
In the movie Blade Runner, “replicants” are engineered biological copies of humans. They are implanted with memories that aren’t real (to them anyway, they are sometimes the recorded memories of other people) in order to provide a sort of replacement to their emotions. The replicants are meant to work in outer space and are illegal on earth. The ones that manage to get to earth are hunted down by Deckard and other blade runners. In order to determine who is a replicant and who is a “real person” blade runners use a “Voight-Kampff” test that measures respiration, heart rate, blushing and eye movement in response to emotionally provocative questions. Today we are going to turn Neo4j into a blade runner and use it to find and retire replicated identities in our data.
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A couple of weeks ago I showed you how to build a dynamic rule based decision tree in Neo4j. It was pretty simple and used an Expression Evaluator to determine if a set of parameters in an expression was true or false. Based on that answer it decided where to go.
But what if we had more than just true or false? What if we wanted to make our Rule nodes have more than 2 options? Today I am going to show you how to do just that… but please make sure you have read part 1 already.
