## What happened before

As  you may remember, I created a little beer graph some time ago to experiment and have fun with beer, and graphs. And yes, I have been having LOTS of fun with it - using it to explain graph concepts to lots of not-so-technical folks, like myself. Many people liked it, and even more people had some questions about it - started thinking in graphs, basically. Which is way more than what I ever hoped for - so that's great!

One of the questions that people always asked me was about the model. Why did I model things the way I did? Are there no other ways to model this domain? What would be the *best* way to model it? All of these questions have somewhat vague answers, because as a rule, there is no *one way* to model a graph. The data does not determine the model - it's the QUERY that will drive the modelling decisions.

One of the things that spurred the discussion was - probably not coincidentally - the AlcoholPercentage. Many people were expecting that to be a *property* of the Beerbrand - but instead in my beergraph, I had "pulled it out". The main reason at the time was more coincidence than anything else, but when you think of it - it's actually a fantastic thing to "pull things out" and normalise the data model much further than you probably would in a relational model. By making the alcoholpercentage a node of its own, it allowed me to do more interesting queries and pathfinding operations - which led to interesting beer recommendations. Which is what this is all about, right?

## Taking the AlcholPercentage to the next level

So in my new version of my beergraph, I have done something different. I used the example of Peter to create an in-graph index of AlcoholPercentages - a bit like the picture of the new model that you see here.

Essentially what I am doing is I am connecting all the alcohol-percentages into a chain of alcholpercentages - using the [:PRECEDES] relationship. In Cypher-style ascii-art that would be something like

... -(alcperc-0.2)-[:PRECEDES]->(alcperc-0.1)-[:PRECEDES]->(alcperc)-[:PRECEDES]->(alcperc+0.1)-[:PRECEDES]->(alcperc+0.2)- ...

To do this, I of course did have to modify my beer-spreadsheet a little bit. You can find the new version over here. But from the screenshot below you can see that all I did was create another tab that had all the alcoholpercentages and that "PRECEDES" relationship between them. Easy peasy.

Nice. So what? The resulting dataset is very similar to what we had before - it's just a little bit richer. You immediately notice it as you start "walking" the graph on the WebUI: the links to the AlcoholPercentage-chain gives me a new and interesting way to explore the graph.

But what else what can we do with this? Well, querying it is the obvious answer. Let me give you a couple of examples:
• how can I find beers that have the same beertype and a "same or similar" alcoholprecentage (let's say + or - 1%) as a beer that I really like (Orval). That's now become very easy:

start
orval=node:node_auto_index(name="Orval")
match
orval-[:IS_A]-beertype,
orval-[:HAS_ALCOHOL_PERCENTAGE]-alcperc,
alcperc-[:PRECEDES*0..10]-otheralcperc,
otherbeer-[:HAS_ALCOHOL_PERCENTAGE]-otheralcperc,
otherbeer-[:IS_A]-beertype,
otherbeer-[:BREWS]-otherbrewery
return
otherbeer.name, beertype.name, otherbrewery.name;

Or another example:

• how can I find other beers from the same brewery that have a similar AlcoholPercentage as a beer that I also like (Duvel)
start
duvel=node:node_auto_index(name="Duvel")
match
duvel-[:BREWS]-brewery,
duvel-[:IS_A]-beertype,
duvel-[:HAS_ALCOHOL_PERCENTAGE]-alcperc,
alcperc-[:PRECEDES*1..10]-otheralcperc,
otherbeer-[:HAS_ALCOHOL_PERCENTAGE]-otheralcperc,
otherbeer-[:IS_A]-otherbeertype,
otherbeer-[:BREWS]-brewery
return
otherbeer.name, otherbeertype.name, brewery.name,
otheralcperc.name
order by
otherbeer.name;

Both of the queries above gave me some new, interesting insights that I did not know before, allowing me to discover even more and nicer Belgian beers. But what's important is of course that these in-graph indexes are fantastically interesting. By "pulling the data out", normalising even further, and then indexing the normalised data as a subgraph of it's own, we can much more easily derive new and interesting insights. And that, my dear friends, is what graphs are all about :) ...

Hope this was useful. If you like this post and want to discuss more about graphs and beer, please come to our Graph Café in June in Antwerp or Amsterdam - or at a pub near you?

## Let's expand the Graph - with Beer

I guess I can no longer keep it a secret: I really do like beer. And Graphs. So every opportunity I get I will try to talk about both. Try to shut me up - it won't work ;-) ... So that's why we are going to try something different in Belgium and The Netherlands, to get more and more people excited about Graphs - with beer. On the 11th and 12th of June, we'll be organising two very nice events at fantastic locations to get our community together, and to get it to *grow*.  So let me tell you a bit more about that.

### A Knowledge Café

The idea is simple: let's all get together and exchange ideas, experiences, expertise, knowledge about what we have been doing with Graphs and Graph databases. Everyone will have the opportunity to sit at a table, grab pens, paper, laptops, or whatever tool you want - and explain one topic or the other about Graphs to the other attendees.

Let's get everyone who has done projects, research, investigations, proof-of-concepts, or why not, product development with graphs together to discuss and learn from one another. Every 15 minutes there will be the opportunity for the most experienced attendees to stand up and do a "lightning talk". 5 minutes about a topic - and if you're interested there's always the possibility to continue the conversation at the knowledge tables.

### At a cool location

Yes - the venues will be absolutely terrific. We picked a couple of very cool, easily accessible locations that - of course - have a thing or two to do with beer. And yes: you will have the opportunity to taste some of the local specialty beers.

### With your friends and colleagues

Please: help us spread the word and expand the graph community. If you bring a friend to the venue and register that person beforehand, there will be a little token of appreciation waiting for you.

Where and when:

If you are interested, want to get involved, want to participate, want to host a table, want to do a lightning talk - then please get in touch.

Looking forward!