Tag Archives: performance

Calculating the best Rail Road paths in Neo4j

Did you know that Chicago is the most important railroad center in North America? Chicago has more lines of track radiating in more directions than from any other city. The windy city has long been the most important interchange point for freight traffic between the nation’s major railroads and it is the hub of Amtrak, the intercity rail passenger system. You may not realize it, but railroad tracks and graph theory have a history together. Back in the mid 1950s the US Military had an interest in finding out how much capacity the Soviet railway network had to move cargo from the Western Soviet Union to Eastern Europe. This lead to the Maximum Flow problem and the Ford–Fulkerson algorithm to solve it.

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Neo4j Stored Procedures for Devs that don’t know Java (yet)

When I joined Neo4j, I didn’t know how to write Java. I was a SQL developer who knew some Ruby and that’s about it. Luckily I had Michael Hunger, Stefan Armbruster, David Montag and others to help me out. I realize however that you may not be so lucky. So today I’m going to share with you a set of slides to help you start you on your journey of using the full power of Neo4j.
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Dynamic Rule Based Decision Trees in Neo4j – Part 4

So far I’ve only showed you how to traverse a decision tree in Neo4j. The assumption being that you would either create the rules yourself from expert knowledge or via an external algorithm. Today we’re going to add an algorithm to build a decision tree (well a decision stream) right into Neo4j. We will simply pass in the training data and let it build the tree for us. If you are reading this part without reading parts one, two, and three, you should because this builds on what we learned along the way.

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Finding your neighbors using Neo4j

In Mr. Rogers’ Neighborhood, the question “Won’t you be my neighbor?” is an invitation for somebody to be close to you. In graphs, it’s an invitation to traverse. The closest neighbors of a node are those reachable by a single relationship hop, but we can also consider nodes two, three or more hops away our neighbors as well. How can we find them in Neo4j? Using the “star”:
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Multiple origin multiple destination 3 relationships queries for knowledge graphs using Neo4j

The multiple-origin-multiple-destination (MOMD) problem is an NP-Hard problem sometimes seen in logistics planning where paths can stretch out really far. A far simpler problem presents itself when we limit the size of the paths. Now you may be wondering, why would we do that? Well… outside logistics we have plenty of graphs where relevance drops as we get further and further away. Think about an Article on Wikipedia. It has links to many other articles that are relevant, and those have links to other articles that are relevant to them but less relevant to our starting Article, and those have links to other articles that may be relevant to them, but have very little to do with our starting Article. I think if we keep going we end up in Philosophy or something like that.
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Building a Dating site with Neo4j – Part Seven

Now it is time to create the timeline for our users. Most of the time, the user wants to see posts from people they could High Five in order to elicit a conversation. Sometimes, they want to see what their competition is doing and what kind of posts are getting responses… also who they can low five. I don’t think they don’t want to see messages from people who are not like them and don’t want to date them but I could be wrong.
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Building a Dating site with Neo4j – Part One

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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Adding gRPC to Neo4j

You are probably sick of me saying it, but one of the things I love about Neo4j is that you can customize it any way you want. Extensions, stored procedures, plugins, custom indexes, custom apis, etc. If you want to do it, then you can do it with Neo4j.

So the other day I was like what about this gRPC thing? Many companies standardize their backend using RESTful APIs, others are trying out GraphQL, and some are using gRPC. Neo4j doesn’t support gRPC out of the box, partially because we have our own custom binary protocol “Bolt”, but we can add a rudimentary version of gRPC support quite easily.
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Stored Procedure to Import Data

A while back I showed you how to write an extension to import the MaxMind city data set. Today is just a repeat of that exercise but instead of using an extension, we will use a stored procedure.

The documentation spells out how to write your own procedures in Chapter 6 so I’m not going to go over that again, but I do want to point out a few things.
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Mutual Fund Benchmarks with Neo4j

Just the other day I had a conversation with an Investment Risk Manager about one of the data problems his team was working on and he was wondering if Neo4j could help. Imagine you have about 20,000 mutual funds and etfs and you want to track how they measure up against a benchmark like say the returns of the S&P 500. I’m sorry did I say one? I meant all of them, let’s say 2,000 different benchmarks… and you want to track it every day, for a rolling 5 years period. So that’s 20,000 securities * 2000 benchmarks * 5 years * 252 trading days a year (on average)… or 50 billion data points. That’s a BIG join table if we were using a relational database. How can we efficiently model this in Neo4j?
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