Part 3/3 - Wikipedia Clickstream analysis with Neo4j - some Graph Data Science & Graph Exploration

In the previous blogposts, I have tried to show

• How easy it is to import the Wikipedia Clickstream data into Neo4j. You can find that post over here.
• How you can start doing some interesting querying on that data, with some very simple but powerful Cypher querying. You can find that post over here.

In this final blogpost I want to try to add two more things to the mix. First, I want to see if I can do some useful "Graph Data Science" on this dataset. I will be using the Neo4j Graph Data Science Library for this, as well as the Neuler Graph App that plugs into the Neo4j Desktop. Next, I will be exposing some of the results of these Graph Data Science calculations in Neo4j's interactive graph exploration tool, Neo4j Bloom. So let's do that. 

Installing/Running the Graph Data Science Library

Thanks to Neo4j's plugin architecture, and the Neo4j Desktop tool around that, it is now super easy to install and run the Graph Data Science Libary - it installs in a few clicks and that you are off to the races:

Part 2/2: Graphs are Bloom-ing

Earlier I wrote about how I connected the newly announced preview version of Neo4j Bloom to my good old faithful Belgian BeerGraph. See part 1 of this 2-part series for that story. I actually split up the story into two parts, because I feel like there's a super interesting and powerful part to Bloom that deserves a bit more attention: the mechanism of the custom Search Phrases.

As we mentioned in the previous post, Bloom structures your exploration and discovery into specific "views" on the graph data, called "Perspectives. You can select the perspective you find most appropriate from a dropdown - and customize/tweak/create perspectives yourself if you are not happy with the auto-generated starting point.

Part 1/2: Graphs are Bloom-ing

Last week something happened that really excited me. We, Neo4j, finally announced Bloom and demonstrated our own Graph Visualisation and Discovery tool, Neo4j Bloom. This is a technology that we have long been pondering, have experimented with in a number of ways, and have long looked to find and develop an offering that would be interesting and differentiated in what is already a very well looked-after marketplace. 

I am not exaggerating when I say that is truly exciting. Not only do many of our customers want to be able to visualise the results of their graph queries, but the graph data model is also unique in the way that it provides such an intuitive, easy to understand data model that lends itself so well to a GRAPH-ical representation. It truly fits into the Graph Platform vision that Neo4j has been advocating since 2017.

Podcast Interview with Kevin Madden, Tom Sawyer Software

OMG has summer flown by. It has been a fantastic season over here in Europe, with lots of great family time and lovely trips to different destinations across Europe - I had a blast.

However, the downside of all this fun has been that I have really not had the time or inclination to publish more podcast episodes. In fact, I have to apologize to the guest on this episode that I am publishing today, the super-smart and fun Chief Software Engineer of Tom Sawyer Software, Kevin Madden - because I actually recorded this episode back in June already!!! Seems like an eternity ago - but at the end of June I was just really running short on time, did not find it possible to publish the interview then, and then... summer sunshine got in the way.

But hey - better late than never! So here's a great interview with Kevin - as you would expect, he has many great and interesting perspectives (pun intended!!!). Here's our chat:

Podcast Interview with Sébastien Heymann, Linkurious

As I am coming up on my 5th anniversary working for Neo4j, I am increasingly happy, proud and thankful for the journey that we had - and the many great people that I have met along the way. One of these people is FINALLY appearing on this podcast, and has a history with this blog every since the VERY first article that I wrote in january 2013: in this article, I showed folks how to load the Belgian Beer Graph into Neo4j using a tool that was actually not intended for this use: Gephi. Many beer (related article)-s later, I am now finally talking to Sébastien Heymann, founder and CEO of Linkurio.us, and one of the main people behind Gephi at the time. Here we go:

Podcast Interview with Clare Zutz and Mark Hand, University of Texas

A few weeks ago I was introduced to a couple of community members that had written a super interesting blogpost about visualising the Global Impact Investing Network (GIIN) on the Neo4j Blog: Clare Zutz and Mark Hand are both researchers at the University of Texas, and as you will read below, they have done some remarkable work to use Neo4j for the greater social good - so read on and follow the conversation...

Here's the transcript of our conversation:

RVB: 00:03 Hello everyone. My name is Rik Van Bruggen from Neo, and here I am recording another episode for the Graphistania podcast, and tonight I have invited two lovely people from all the way in Texas, who have been doing some wonderful work with Neo4j. That's Mark Hand and Clare Zutz. Hi guys. 

CZ: 00:24 Hello.

Podcast Interview with Karl Urich, Datafoxtrot

Been a hectic couple of weeks, which is why I am lagging behind a little bit in publishing lovely podcast episodes that I actually recorded over a month ago. Here's a wonderful and super-interesting chat with Karl Urich of DataFoxtrot, who wrote about graphs, spatial applications and visualisations recently on our blog and on LinkedIn Pulse. Lovely chat - hope you will enjoy as much as I did:


Here's the transcript of our conversation:

RVB: 00:01 Hello, everyone. My name Rik. Rik Van Bruggen from Neo Technology, and here I am, again, recording another episode of the Graph Database podcast. Today, I've got a guest all the way from the US, Karl Urich. Hi, Karl. 

KF: 00:15 Rik, very nice to speak with you. 

RVB: 00:17 Thank you for joining us. It's always great when people make the time to come on these podcasts and share their experience and their knowledge with the community, I really appreciate it. Karl, why don't you introduce yourself? Many people won't know you yet. You might want to change that. 

KF: 00:35 Yeah, absolutely. So, again, thanks for having me on this podcast. It's really great to be able to talk about the things I have experimented with and see if it resonates with people. I own a small consulting business called DataFoxtrot, started under a year ago. Primary focus of the business is on data monetisation. If a company has content or data, how can we help those companies make money or get new value from that content or data if they could be collecting data as a by-product of their business or they could be using data internally in their business and then they realise that someone outside the company can use that as well? So, that's the primary focus of my business, but like any good consulting company, I have a few other explorations and really this intersection of the world of graph and spatial analytics or location intelligence is what interests me. So, talking a little bit about those explorations is what will hopefully interest your listeners. 

RVB: 01:38 Yeah, absolutely. Well, so, that's interesting, right? I mean, what's the background to your relationship to the wonderful world of graphs then, you know? How did you get into it? 

KF: 01:45 Yeah, so going all the way back to college, I did take a good Introduction to Graph Theory as a mathematics elective, but then really got into the world of spatial and data analytics.  For 20 years working with all things data: demographic data, spatial data, vertical industry data, along the way building some routing products, late 1990's or late 2000's products, that did point to point routing, drive time calculations, multi-point routing. Really kind of that original intersection of graph and spatial. But, data junky, very interested in data: graph, spatial, data modelling et cetera. 

RVB: 02:28 Yeah. Cool. I understand that these spatial components is like your unique focus area, or one of your at least focus areas these days, right? Tell us more about that. 

KF: 02:39 Yeah, absolutely. And it's certainly what resonates when I think of about the graph side, spatial data really should define-- spatial data could be any sort of business problems related to proximity location or driving things because you know where something is, your  competitors, your customers, the people that you serve. And that's where it resonated to me when, as I start to look at graph and spatial, I was really excited back in April. I walked in, just very coincidentally, in a big data conference to a presentation being put on by Cambridge Intelligence-- 

RVB: 03:24 Oh, yeah. 

KF: 03:26 And so they were introducing spatial elements to their graph visualization. 

RVB: 03:31 That's really-- they just released a new product, I think. Right?

KF: 03:34 Just released the new product, at the time had gone beta. So, that really got me thinking about how could you combine graph and spatial together to solve a problem. Looking at Cambridge Intelligences, technology of looking at some spatial plugins for Neo, and again, my company is a consulting company and if there is a need for that expertise at the intersection of graph and spatial, we want to explore that. 

RVB: 04:05 Very cool. Did you do some experiments around this as well, Karl? Did you, sort of, try to prove out the goals just a little bit? 

KF: 04:11 Yeah. Absolutely. Let me talk a little bit about that. At this concept of combined spatial and graph problem that looked at the outliers, outliers just meaning things that are exceptional, extraordinary, and the thinking is, in my mind, was businesses and organisations can get value from identifying outliers and acting on those outliers. So, maybe an outlier can represent an opportunity for growth by capitalising on outliers, or bottom-line savings by eliminating outliers. Let me give an example of an outlier. If you look at a graph of all major North American airports, and their flight patterns, and put it on a map, you could visualise that Honolulu and Anchorage airports are outliers. There are just few other airports that, "look the same”, meaning same location, same incoming and outgoing flight patterns. And that's really relatively easy if you have a very small graph to visualise outliers, but if you want to look at a larger graph, hundreds of thousands, millions of nodes, what would you do? So, that really started the experiment. I was looking around for test data. Wikipedia is fantastic. You can download-- 

RVB: 05:28 [chuckles] It is. 

KF: 05:29 Wikipedia data-- I love Wikipedia. Anyway, it seemed very natural. And the great thing is that there are probably around a million or so records that have some sort of geographic tagging. 

RVB: 05:42 Oh, do they? 

KF: 05:44 Yep, so a page-- London, England has a latitude longitude. Tower of London has a latitude and longitude. An airport has a latitude longitude. 

RVB: 05:54 Of course. 

KF: 05:54 So, you can tease out all of the records that have latitude longitude  tagging, preserve the relationships and shove that all into a graph. So, you have a spatially enabled graph, every XY has a-- every page has a latitude longitude or XY. So, really the hard work started, which was taking a look at outliers. So, quick explanation of outliers, so, you think of  a Wikipedia page for London, England, a Wikipedia page for Sidney, Australia, they cross reference each other. Pretty unusual to locations other side of the world, but would you call those outliers? Not really, because there's also a relationship between the London page and the Melbourne, Australia Wikipedia page. So, you really wouldn't call those anything exceptional. And so, what I built was  a system, or just a very brief explanation is that I looked at relationships in the graph, looked only at the bi-directional or bilateral relationships where pages cross-referenced each other. None have really identified how close every relationship was to another relationship or looked for the most spatially similar relationship. You can score them then, and you can kind of rank outliers. So, let me just give one quick example. It's actually my favorite outlier that I've found-- 

RVB: 07:30 Which category? 

KF: 07:31 Unusual thing to say. There's a small town in Australia called Arish. I think I'm pronouncing that right, that has a relationship with the town in the Sinai Peninsula called Arish, and El Arish in Australia is named after Arish, Egypt because Australian soldiers were based there in World War One-- 

RVB: 07:51 No way! 

KF: 07:53 Yep! And most importantly, this relationship from a spatial perspective, looks like no other relationship. So, that's the kind of thing, when you are able to look at relationships, try to rate them in terms of spatial outliers-- 

RVB: 08:10 Yeah, sure. 

KF: 08:12 You can find things that lead to additional discovery as well. 

RVB: 08:18 Super cool. 

KF: 08:19 As a Wikipedia junkie, that's pretty fascinating. 

RVB: 08:21 [laughter] Very cool. Well, I read your blog post about-- outliers made me think of security aspects actually. I don't know if you know the book Liars and Outliers. It's a really great book by Bruce Schneier. I also have to think about-- we recently did a Wiki Wiki challenge, which is, you know, finding the connections between Wikipedia pages. You know, how are two randomly chosen Wikipedia pages linked together, which is always super fun to do. 

KF: 09:00 It was even in my original posting and I didn't want to say that, "Hey, this could be used for security type applications." So, I think I talked in code and said, "You could use this to identify red flag events," but I like to think of it as both the positive opportunity and the negative opportunity when you're able to identify outliers and-- 

RVB: 09:26 Yeah, identifying outliers has lots of business applications, right? I mean, those outliers are typically very interesting, whether it's in terms of unexpected knowledge, or fraudulent transactions, suspect transactions. Outliers tend to be really interesting, right? 

KF: 09:43 Absolutely, absolutely. 

RVB: 09:45 Super cool. So, where is this going, Carl? What do you think-- what's the next step for you and DataFoxtrot, but also graph knowledge in general? Any perspectives on that? 

KF: 09:56 Yeah. So, there's more of a tactical thing, which is as we record a week from now we have GraphConnect probably-- 

RVB: 10:04 I am so looking forward to it. 

KF: 10:06 Which will be fantastic and being able to test this out with people. It's always great to bounce ideas off to people. In terms of our next experiments, the one that interests me is almost the opposite of outliers and let me explain. So, I have some background in demographics, analytics, and segmentation, so, what interests me a lot is looking at clustering of relationships of the graph. Think of clustering is grouping things that are similar in to bins or clusters, so that you can really make over arching statements or productions about each cluster. You can use techniques like K Means to do the clustering. So, what interests me about graph and spatial for clustering is you can use both elements. The relationships of the graph, spatial location of the nodes, together to drive the clustering. I've started some of the work on this and, again,  using Wikipedia data and maybe the outcome, using Wikipedia, if you did your clustering based on spatial location of the nodes, plus strength of the connection, plus the importance of the nodes, plus maybe some other qualifiers, like if a node is a Wikipedia page for a city or a man-made feature, a natural feature, you might end up with clusters that have labels to them. One cluster might be all relationships connecting cities in South America and Western Europe, or relationships between sports teams around the world. So, it's kind of the opposite, if outliers is finding the outliers, the exceptional things, clustering is finding the patterns. 

RVB: 11:42 Commonalities. 

KF: 11:44 A real-world example might be an eCommerce company is looking at the distribution network, and they want to do clustering based on shipments, who shipped what to whom, where the shipper and recipient are, package type, value, other factors, and they could create a clustering system that categorises their distribution network and they can look at business performance by cluster, impact of marketing on clusters and sometimes just the basic visualisation of clustering just often yields those Eureka moments of insight. That's kind of the next entrusting project that's out there. I'd say, ask me in six to eight weeks [laughter]. 

RVB: 12:29 We'll definitely do that. Cool. Carl, I think we're going to wrap up here. It's been a great pleasure talking to you. Thank you for taking the time, and I really look forward to seeing you at GraphConnect. I wish you lots of fun and success with your project. 

KF: 12:49 Excellent. Thank you very much Rik, really appreciate it. 

RVB: 12:51 Thank you, bye bye.

Subscribing to the podcast is easy: just add the rss feed or add us in iTunes! Hope you'll enjoy it!

All the best

Rik

Podcast Interview with Tom Zeppenfeldt, Ophileon

One of the topics in the Graph Database space is that is truly dear to my heart is the visual aspect. Graphs seem to be - for some very deep and profound reason, is my guess - a very natural way for humans to interact with data. And what better way to do that then in a truly visual way.

So I still remember 2+ years ago or something, this guy shows up at our Amsterdam meetup gathering and starts talking to me about the Neo4j browser interface - which was still in its infancy at the time. All of his questions made total sense, and I kindof wish I had had better answers for him at the time. But I kindof also am happy that I didn't, because that's why Tom Zeppenfeldt but on his "working gloves" and got to developing a new tool that looks really promising: Ophileon's Prologram.

Tom has recorded some really nice videos online where he is showing some of the capabilities of Prologram:

• how to work with Virtual Nodes in a graph
• how to enable Controlled Exploration of a graph

So that's when I asked him to come on the podcast too - and here's the result:





Here's the transcript of our conversation:

RVB: Hello everyone. Here we are again, recording another episode for our Neo4j Graph Database podcast. My name is Rik Van Bruggen. I work for Neo Technology, and on the other side of this Skype call, all the way in the Netherlands, is Tom Zeppenfeldt. Hi Tom.

TZ: Hi Rik. How are you doing?

RVB: I’m doing very well. How about yourself?

TZ: Yes. I'm very fine.

RVB: [chuckles] That's great. Well, Tom, most people who are listening to this Podcast probably don't know you yet, so would you mind introducing yourself? Who are you, what do you do, and what's your relationship to the wonderful world of graph databases?

TZ: Okay. Well, my background is not in IT. By education I'm an agricultural engineer and the main field where I've worked over the last couple of years was in international development in Africa and Latin America and that's also where there is a link between-- the link with Neo4j.

RVB: Oh, no way. Tell me about that.

TZ: Yes, as you probably know and there are lots and lots of types of development projects in agriculture, infrastructure, education and health for instance, and one of the typical things that you use in that context is what they call Result Chains, so diagrams that link activities and organizations and results and impacts and effects to each other and also a concept that is called Actor Constellation Mapping. That is a way of diagramming all the interests of different stakeholders in a project and how people collaborate, how they form networks, and  when I say networks I'm already talking graphs.

RVB: Yes, absolutely. Wow, that's great. How long ago did you first encounter Neo and how did that...?

TZ: That happened about four years ago and when we ran into a very concrete situation where we had to make these kinds of diagrams accessible over the Internet. So instead of drawing Powerpoints with graph-like structures in it, we were looking for a database platform that would allow us to share these things and make these things interactive over the Internet.

RVB: So, then that sort of created the desire to store them in a network way as well, I suppose.

TZ: Yes.

RVB: Yes. Okay. What are you doing now with Neo, Tom, because I know you're really doing some really fancy stuff, but tell us about that.

TZ: Yes. Well, currently our main project is a project that is partially privately funded and partially subsidized by the dutch government, and which is focusing researchers like journalist that do research or investigations on a specific subject. It include partially Neo4j for storing relations between documents, but also documents and content from social networks and also all kinds of tagging and metadata and for that, we have also a corporation with two universities in the Netherlands  that help us to automatically classify and tag documents or to detect relationships inside documents.

RVB: Wow, that sounds really great.

TZ: That's the main project we are working on now, yes.

RVB: Wow. As I understand there's also a big visualization component to that project, right. There's a lot of stuff that you're doing on how to bring that information visually to those researchers?

TZ: Yes, and indeed that's in terms of what we develop in terms of software is in fact, you can consider it an enhanced browser for the Neo4j database because once we were starting this project, we were running into-- well, we wanted to add more than the standard Neo4j browser, and that is why we now have a browser environment with multiple panels and panels that you can link to each other and in one panel you can display the data as a network, in other panels you can display data as a table and you can link them to each other, and that's what we call-- this product is called, “Prologram," for now, and that is what a number of developers are working on actually, yes.

RVB: That's very cool.  I've seen it live when you presented it at the meetup and everything, but I've also seen some of the YouTube videos and I'll post those with the podcast, as well.

TZ: Okay, great.

RVB: Maybe, just looking at it in a little bit more detail, what do you think is the most powerful aspect of this?  In other words, why did you end up choosing a graph database for doing this type of a project?  Any comments on that?

TZ: Yes. Well I come from … my background; I once had an IT company myself and that was typically relational databases, and finally we ended up building a metadata layer to mimic graphs on top of it.  In fact, that says it all, because the real world is far more complex than you can model, well in tables. Specifically the domains that I've worked in, be it agricultural development or now investigative journalism.

RVB: Yes.

TZ: If you have a very generic and basic structure like you have in Neo4j where also not just the notes but also the relationships are really things in itself, you call them first class citizens and are very descriptive. It allows you - even when your data model is changing or expanding and that is happens very frequently - You don't have to do a complete overhaul of what you already have to have and still have an optimize structure. So, you can keep on building and adding stuff and that's where for us one of the main advantages of Neo4j and that is on top of, of course the way that you can and really do optimized and very localized searches in your graph database, and I have the experience with building meta-structures on top of relational database then the number of joins is incredible and finally it makes it workable.

RVB: Yes. I don't know if you've listened to any of the other podcast episodes but some of the Neo4j founders have been on there as well and that's how they started Neo4j. They started developing a meta-layer or graph layer on top of-- it was Postgress at the time I believe. I think you're coming to do right conclusion there [chuckles].

TZ: When the...

RVB: Go for it. You wanted to add something?

TZ: Well, for us it was quite easy to pick the concepts, to understand the concepts and that's also I think we now are making nice progress with what we make as a generic browser on top of Neo4j is that we come already,  we came already from a graph, a mindset that knew the advantages of a graph database.

RVB: So what is the future of all this? Tom, where do you think this industry is going, where should Neo4j go? Where is the Prologram going?

TZ: Well, that's the-- You said it's ten minutes, this podcast. [laughter] [crosstalk] RVB: Well, let's limit-- Okay, well, I think graph database are there to stay, it's not the hype. Especially when relations between contents become more and more important than perhaps the properties of content… For instance, we are now working on recommendation engines and while, in the the beginning, people got recommendations on the basis of properties or links inside the content itself, so very explicitly - now  we are already doing test with integrating into recommendation, the social aspects. Then you finally end up concluding that the suggestions that you can make on the basis of how people use the contents and that is typically something that you can easily store in the graph. They are better than the suggestions that you can make on the basis of the explicit tagging of a content.

TZ: As the world gets more and more connected, whether it's Internet of Things or documents that are shared. Well, it's the graph model is a very nice way to describe - you get more exact models than a relational database. I think the position of graph databases in the world is an established one. What we are now doing with the Prologram platform, we have been building it with let's say, about three to four persons of the last 14 to 15 months. We now have a version that is -- on one end it's good enough, but we are; say in the next two to three months we will probably start sharing it with the world because we want to know how people interact with it, and also I'm sure you cannot keep on developing without user feedback. We have already quite some users and in terms of functionality there will be additions like well integration with the elastic search, a complete function and trigger system that allows you to change theories, virtualization of notes that allows you to make aggregations - to visualize aggregation of notes and relationships. So, that's where we are more or less going for the next quarter. That's more or less the road map that we have.

RVB: You discover it as you go. Right?

TZ: Yes.

RVB: It sounds really exciting. We'll wrap it up at that. Tom, it was really great talking to you. Thank you so much for coming online and apologies for the technical hiccups that was entirely my fault. But, thank you for coming online. I'm sure will see each other again at one of the next meet-ups.

TZ: Okay. Thanks for the opportunity and keep up the good work.

RVB: Thank you. Cheers, bye.

TZ: Okay. Bye.

Subscribing to the podcast is easy: just add the rss feed or add us in iTunes! Hope you'll enjoy it!

All the best

Rik

Podcast Interview with Alistair Jones, Neo Technology

Here's another great conversation with a great friend and colleague of mine, Alistair Jones. Alistair has been an Engineer at Neo4j for a number of years now, and has evolved to be one of our most well-known Visualisation experts. He gave a wonderful talk about that a few years ago at the NOSQL Xchange in London (I still love the Fireworks!!!), and has been instrumental in the development of the Neo4j Browser over the years. He's also the author of a great graph drawing tool, Arrows - which I use all the time...

So we had a great talk, live, face to face, and here it is:

Here's the transcription of our conversation:

RVB: Hello everyone. This is Rik - Rik van Bruggen - from Neo Technology, and here I am again recording a podcast session for our Neo4j Graph Database podcast. Today is a live session which is always great. It's unusual these days with Skype and everything. but today I'm here in the same room as Alistair - Alistair Jones - from Neo. 

AJ: Hello Rik. 

RVB: Hey. Good to be here, Alistair. So today I'm going to ask Alistair to introduce himself, right? Because maybe not everyone knows you yet. 

AJ: I should say welcome to London, Rik. Good to have you here. I'm Alistair Jones, I work as an engineer at Neo Technology, and I guess I'm one of the sort of old-timers now. I've been around for coming up to four years at Neo, and I work on the-- I'm an engineer working on product. 

RVB: Very cool. So tell us a little bit about the stuff that you're working on. I know you're one of the big guys behind the visualization elements of the browser. Tell us a little bit more about that. 

AJ: Yeah, sure. One of the big things about graphs is that they're inherently visual. It's really easy to draw a picture of them, and drawing a picture is a really clear way to get across the message of what they're about, also to see how you're data's structured. And you can learn a lot from just looking at a picture of a graph. So visualization is clearly an important area. I've been interested in this from the beginning, from the first day I joined Neo. Being quite a visual person, I could see that we needed to do more in this area, and really get into the visualization side of things. And really, the stuff that I've done-- I had to come in at the Neo4j 2.0 Version which came out a year and a half ago. I was just very interested in it, and it started back-- it made it into the product about a year and a half ago, but it started back for like the first month I was at Neo, and I really wanted to-- I was giving a talk at a conference and I wanted to draw a picture, I looked at the tools that were out there to draw a picture just to go on some slides. Most people use PowerPoint or some kind of drawing package, and I want to do something better, and that got me into making my own tool. 

RVB: Is that how you got into the Arrows project, right? That's what you host on your website, the Arrows tool, right? 

AJ: Yeah, exactly. That's what came out of it, out of that exercise, was a tool just to help you draw a picture of a graph. I have these little Arrows tool on my personal website and it was really for making these PowerPoint stark-style pictures better than what PowerPoint can do. 

RVB: I can tell you, I use it all the time. It's really [chuckles] great. I love it. How did you get into graphs, Alistair, and then what do you really love about graphs? Can you tell us a little bit more about that? 

AJ: Yeah. I actually got into graphs properly on joining Neo. I knew a lot of the people who I work with still are here at Neo from previous jobs, and said-- they were hugely excited about this, I kind of followed what was going on, and then it was like, "Well, Alistair, you should come and do some of this stuff here. You've got the right kind of things to work in this space." And then, I came from a software direction, but with my background in consulting and helping lots of different businesses do stuff with databases in general, I could see what a good fit it was. So, yeah, especially all those applications where-- I struggled for a long time with building application on top of relation databases, a lot of the modeling complexity and the technical challenges of getting things to perform in that world. Then I could easily see where the value comes from in the graph world and having that-- reducing the impedance mismatch between writing software and object-oriented language, and then persisting it to the database as a graph makes an enormous difference. 

RVB: It's something that we've heard a couple of times already on the podcast - the modeling advantages and all those things. It's great to sort of hear it from you from a practitioner's point of view, as well. We want to keep these conversations quite short, so to wrap it up a little bit, where do you think it's going? What are the big things on the horizon you think in the graph database space? 

AJ: I think it's about adoption. I think about people seeing it becoming even more popular. People just going straight for graph first as the way - the natural way - to model their data. You're making your application, something simple, something very ambitious, and you go, "Well, obviously, we'll go to look at this in a graph way first." And I think that will just accelerate everything that's going in the space, the whole community around this is really a big help. 

RVB: Any exciting new things you see on the visualization horizon? 

AJ: Yeah, I think so. [chuckles] I don't really want to burn myself-- 

RVB: Course not, no. 

AJ: --with saying lots of exciting things to come up. I think in terms of what we're doing, and also in terms of what our partners are doing around visualization as well, is very interesting. There's a whole product space out there, and some really interesting research, and new things coming online all the time. 

RVB: Absolutely. We've had a Jean Villedieu from Linkurious on the podcast already and I'm sure we'll talk to other people as well. Thank you so much for coming on the podcast - really appreciate it. It was great talking to you as always, and I look forward to working with you in the future. 

AJ: Cheers.

Subscribing to the podcast is easy: just add the rss feed or add us in iTunes! Hope you'll enjoy it!

All the best

Rik