cleishm

= graphGist generated from spock test Neo4jCypherSameSchoolInfluence.groovy

graphGist asciiDoc file for use at http://gist.neo4j.org/ [GitHub Gist]

Generated on Sun Jul 28 08:03:44 PDT 2013

//console


Initialize Graph

cleishm

= graphGist generated from spock test Neo4jCypherOneRelationship.groovy

graphGist asciiDoc file for use at http://gist.neo4j.org/ [GitHub Gist]

Generated on Mon Jul 29 07:11:04 PDT 2013

//console


query to create plato with philosopher label

cleishm

= graphGist generated from spock test Neo4jCypherOneLabel.groovy

graphGist asciiDoc file for use at http://gist.neo4j.org/ [GitHub Gist]

Generated on Sun Jul 28 08:03:43 PDT 2013

//console


query to create plato with philosopher label

cleishm

Orienteering Gist

This is the Orienteering Dataset based on the blog.neo4j.org post.

It’s a simple, three-leg training course in an Antwerp park. Setting this up as a graph in neo4j was easy enough:

cleishm

= Last.fm Dataset Gist =

Earlier this month, I published http://blog.neo4j.org/2013/07/fun-with-music-neo4j-and-talend.html[a blog post] about my fun with some self-exported http://last.fm[Last.fm] data. With this Gist, I would like to provide a bit more practical detail on the dataset and how you could use it.

Let's first create an overview graph of the model.

image::http://2.bp.blogspot.com/-uNPggNP9A3c/Ud7HDhwpkbI/AAAAAAAAAK4/AZd25Q0h-j4/s640/Screen+Shot+2013-07-11+at+16.52.59.png[]

[source,cypher]
----

cleishm

= Business Rule / Recommendation gist =
 
In this simple example, we want to highlight the power of graphs to describe, discover, visualise and implement powerful business rule-based recommendations.

In the example, we will create a simple graph containing
	- a +person+ ("Rik")
	- a +city+ ("London")
	- an +age+ ("39")
	- a +child+ ("Toon")
and all the required relationships from the person to the city, to his age, and his child. 

cleishm

= Why JIRA should use Neo4j

== Introduction

There are few developers in the world that have never used an issue tracker. But there are even fewer developers who have ever used an issue tracker which uses a graph database. This is a shame because issue tracking really maps much better onto a graph database, than it does onto a relational database. Proof of that is the https://developer.atlassian.com/download/attachments/4227160/JIRA61_db_schema.pdf?api=v2[JIRA database schema].

Now obviously, the example below does not have all of the features that a tool like JIRA provides. But it is only a proof of concept, you could map every feature of JIRA into a Neo4J database. What I've done below, is take out some of the core functionalities and implement those. 

== The data set

cleishm

= Enterprise Content Management with Neo4j

== Introduction

There are several challenges in Enterprise Content Management (ECM) that current technologies cannot tackle efficiently. With Neo4j, a whole new world of possibilities opens up. There are few things more "graphy" than ECM, and so the logical next step is the use of graph databases.

What follows is a subset of the possibilities with Neo4J in ECM. We tackle recommendations, time-based versioning, ACL, metadata management and user action registration. 

== The dataset 

cleishm

= Models Sports Leagues
Aravind R. Yarram <yaravind@gmail.com>
v1.0, 08-Sep-2013

== Domain Model

Each *League* has multiple *Level*s like playoffs, quarter-finals etc. The levels are ordered: first is playoffs, +NEXT+ is quarter-finals, +NEXT+ is semi-finals and then the next and last one is the finals. The ordering is represented using a http://docs.neo4j.org/chunked/milestone/cookbook-linked-list.html[linked-list].

A *Player* can play for more than one team over multiple leagues but can only play for a single team in a given league. This is captured by the +PLAYED_IN_FOR_LEAGUE+ http://docs.neo4j.org/chunked/milestone/cypher-cookbook-hyperedges.html[hyperedge] between player, team and league using http://docs.neo4j.org/chunked/milestone/cypher-cookbook-hyperedges.html[hypernode] *PlayerTeamLeague* . A team can register in a new league with a different name in which case, we want to know what it was +PREVIOUSLY_KNOWN_AS+.The fact that a player had for a given team (irrespective of which league) is capture

cleishm

Mystery Science Theater 3000 Actors and Characters - GraphGist for the Neo4j GraphGist challenge

This Graph is based on the MST3K TV-series that ran during the 1990s. Awesome TV-serie, my favourite actually. I created this Graph based on the characters of the show and where they live/reside/hunt, and which actors played them. As the Actors usually played several characters, and many characters were played by several actors, the graph get’s a bit interesting :) Enjoy!