rvanbruggen

***
*** SHOW ME THE IN-GRAPH-ALCOHOLINDEX
***

start
  alcperc=node:node_auto_index(type="AlcoholPercentage")
match
	p=(alcperc-[:PRECEDES]->())
return p;

rvanbruggen

*************************************************
**** BEER DATASET - CYPHER Query Examples ****
*************************************************


****  Beers from same brewery as DUVEL ****

START duvel=node:node_auto_index(name="Duvel")
MATCH
  duvel<-[:BREWS]-brewery,

rvanbruggen

******************************************
*** Some cool ConceptNet Graph Queries ***
****************************************** 

*** Look up a concept in the Graph *** 

START beer=node:Concepts(id="/c/en/beer")
return beer;

*** Figure out the relations of a concept to other concepts ***

rvanbruggen

= My simple BeerGraph Datamodel =

Let's see if I can create a graphgist for my beers

[source,cypher]
----
CREATE (brewery:brewery{name:'Brewery'}), (beerbrand:beerbrand{name:'BeerBrand'}), (alcperc:alcoholpercentage{name:'Percentage'}), (beertype:beertype{name:'BeerType'})
CREATE (alcpercbefore:alcoholpercentage{name:'PercentageBefore'}), (alcpercafter:alcoholpercentage{name:'PercentageAfter'})
CREATE brewery-[:BREWS]->beerbrand<-[:HAS_ALCHOLHOL_PERCENTAGE]-alcperc
CREATE alcpercbefore-[:PRECEDES]->alcperc-[:PRECEDES]->alcpercafter

rvanbruggen

= 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. 

rvanbruggen

= 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]
----

rvanbruggen

#!/usr/bin/env python
# encoding: utf-8
"""
linkedin-query.py

Created by Thomas Cabrol on 2012-12-03.
Customised by Rik Van Bruggen
Copyright (c) 2012 dataiku. All rights reserved.

Building the LinkedIn Graph

rvanbruggen

// Find Shortest paths between two contacts

START
	n=node:node_auto_index(name='Emil Eifrem'),
	m=node:node_auto_index(name='Steven Noels')
MATCH
	p = AllShortestPaths(n-[*]-m)
RETURN p;

// Find all the relationships between two "first degree" contacts of RVB

rvanbruggen

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. The graph is really simple: it contains * the Controls of the simple course that I have here * the different route choices (in the form of a series of Waypoints) that an Orienteer could make to go from start to control point, to control point, to finish. Each of the "legs" of the course, and every route choice of that "leg", has a certain distance and runnability score (value from 0,1 to 1). We are thus dealing with a classic pathfinding problem, where we want to find the best route choice to complete the course.

rvanbruggen

create ({id:'1',name:'DeepPAX',type:'ShopArticle'});
create ({id:'112996',name:'Screw',type:'Component'});
create ({id:'124593',name:'Nail',type:'Component'});
create ({id:'113434',name:'ScrewLock',type:'Component'});
create ({id:'101375',name:'WoodStick',type:'Component'});
create ({id:'100402',name:'Screw',type:'Component'});
create ({id:'100644',name:'Screw',type:'Component'});
create ({id:'100347',name:'Screw',type:'Component'});
create ({id:'103693',name:'LockPlate',type:'Component'});
create ({id:'123773',name:'CornerCover',type:'Component'});