Michael Hunger jexp

Finding movies by decade

First let’s load in the movies data set:

The World Cup Graph

Initial Data Setup

As it is quite a lot of data to import into the in-memory instance this might take a bit, please bear with us.

The OSCON Graph-in-a-Gist

At Neo4j, we love going to

This year, there is a DYI Schedule page that we are very intrigued by, so let’s build a — graph of the conference!

The Model

Network Setup

create (_0:`Subnet` {`cidr`:"1.0.0.240/30", `name`:"one-alpha"})
create (_1:`Subnet` {`cidr`:"1.0.0.32/29", `name`:"one-branchalpha"})

CREATE (you:Person {name:"You"})-[like:LIKE]->(us:Database:NoSql:Graph {name:"Neo4j" }),
       (him:Person {name:"He"})-[l:LIKE]->(us)
RETURN you, like, us

And render as a graph.

Discover Graph Databases with Neo4j and PHP

Graph databases are now the heart of a lot of running companies dealing with high connected data.

Business graphs, social graphs, economic graphs are daily encountered words when browsing the web.

Neo4j, the world's leading graph database developed by NeoTechnology, has proven her natural ability to deal with massive amount of high connected data.

The recent use cases from TomTom and Ebay's Shuttle demonstrated the value she will bring allowing your company to provide fantastic customer experience and processing complex queries in no time.

Discover Graph Databases with Neo4j and PHP

Graph databases are now one of the core technologies of companies dealing with highly connected data.

Business graphs, social graphs, knowledge graphs, interest graphs and media graphs are frequently in the (technology) news. And for a reason. The graph model represents a very flexible way of handling relationships in your data. And graph databases provide fast and efficient storage, retrieval and querying for it.

Neo4j, the most popular graph database, has proven that ability to deal with massive amount of high connected data in many use-cases.

The GOTO Amsterdam Schedule

This gist is a graph version of the GOTO Amsterdam Schedule that I imported into Neo4j.

The GOTO Amsterdam Graph

First we have to load the data into the graph. This was a bit of work - but not difficult at all:

Maze with locked doors

Imagine a maze with rooms (nodes) and doors (relationships) between the rooms. The doors to some rooms are locked and their keys are held in other rooms (as properties).

In this gist model of the maze, you can reach room D by following the path A→B→C→A, picking up the key for room D in room B. However it is not possible to reach room F, since the key to room F is in room E, and the key to room E is in room D, and there is no way back from room D to room A, so room E is inaccessible. (Finding the HIDDEN door from room D to room C would make this possible.)

Is it possible to create queries which take into account the keys that can be picked up on the way? For example: