Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

The first GraphGist Challenge completed by Anders Nawroth.

From the post:

We’re happy to announce the results of the first GraphGist challenge.

First of all, we want to thank all participants for their great contributions. We were blown away by the high quality of the contributions. Everyone has put in a lot of time and effort, providing thoughtful, interesting and well explained data models and Cypher queries. There was also great use of graphics, including use of the Arrows tool.

We thought we had high expectations, but the contributions still exceeded them by far. In this sense, everyone is a winner, and we look forward to sending out a cool Neo4j t-shirt and Graph Connect ticket or a copy of the Graph Databases book to all participants. And for the same reason, we strongly advice you to go have a look at all submissions.

The winners:

At third place, we find Chess Games and Positions by Wes Freeman. He makes it all sound very simple:

…

Learning Graph by Johannes Mockenhaupt comes in at second place. Here’s his own introduction to it:

…

The US Flights & Airports contribution from Nicole White finished first in this challenge. Congrats Nicole!

….

The near future:

If you want to have a look at the GraphGist project, it’s located here: https://github.com/neo4j-contrib/graphgist. It’s a client-side only browser-based application. Meaning, it’s basically a bunch of Javascript files. We’d be happy to see Pull Requests for the project. Please note that you can contribute styling or documentation (as a GraphGist), not only Javascript code!

We already got questions about the next GraphGist challenge. Our plan is to run the next challenge around the time Neo4j 2.0 gets released. Currently we think that will mean a closing date before Christmas. We’ll keep you posted when we know more.

Anders provides great descriptions of the winners but see their entries for full details.

For that matter, see all the entries. The breath of applications may surprise you.

Even if not, it will be good preparation for the next GraphGist challenge!

DocGraph – Neo4j Version by Max De Marzi.

Max has created a Neo4j version of the DocGraph dataset.

Enjoy!

Towards OLAP in Graph Databases (MSc. Thesis) by Michal Bachman.

Abstract:

Graph databases are becoming increasingly popular as an alternative to relational databases for managing complex, densely-connected, semi-structured data. Whilst primarily optimised for online transactional processing, graph databases would greatly benefit from online analytical processing capabilities. Since relational databases were introduced over four decades ago, they have acquired online analytical processing facilities; this is not the case with graph databases, which have only drawn mainstream attention in the past few years.

In this project, we study the problem of online analytical processing in graph databases that use the property graph data model, which is a graph with properties attached to both vertices and edges. We use vertex degree analysis as a simple example problem, create a formal definition of vertex degree in a property graph, and develop a theoretical vertex degree cache with constant space and read time complexity, enabled by a cache compaction operation and a property change frequency heuristic.

We then apply the theory to Neo4j, an open-source property graph database, by developing a Relationship Count Module, which implements the theoretical vertex degree caching. We also design and implement a framework, called GraphAware, which provides supporting functionality for the module and serves as a platform for additional development, particularly of modules that store and maintain graph metadata.

Finally, we show that for certain use cases, for example those in which vertices have relatively high degrees and edges are created in separate transactions, vertex degree analysis can be performed several orders of magnitude faster, whilst sacrificing less than 20% of the write throughput, when using GraphAware Framework with the Relationship Count Module.

By demonstrating the extent of possible performance improvements, exposing the true complexity of a seemingly simple problem, and providing a starting point for future analysis and module development, we take an important step towards online analytical processing in graph databases.

The MSc. thesis: GraphAware: Towards Online Analytical Processing in Graph Databases.

Framework at Github: GraphAware Neo4j Framework.

Michal laments:

It’s not an easy, cover-to-cover read, but there might be some interesting parts, even if you don’t go through all the (over 100) pages.

It’s one hundred and forty-nine pages according to my PDF viewer.

I don’t think Michal needs to worry. If anyone thinks it is too long to read, it’s their loss.

Definitely going on my short list of things to read in detail sooner rather than later.

Building Applications with a Graph Database by Tobias Lindaaker.

Slides from JavaOne 2013.

Hard to tell without a video how much is lost by having only the slides.

However, the slides alone should make you curious (if not anxious) about trying to build an application based on a graph database.

Neo4j centric in places but not unexpectedly since it is hard to build an application in the abstract.

At least if you want visible results. 😉

Over one hundred (100) slides that merit a close read.

Enjoy!

GraphGist Wiki

Quite naturally after I posted Why JIRA should use Neo4j I discovered it is part of a larger mother lode of graph gists!

You will find entries for the GraphGist Challenge, examples, graph design problems, tutorials, fun graph gists and philosopher graph gists.

One of the fun graphs is about Belgian beer, which lead me to: Everyone loves beer. Several Neo4j projects about beer.

Late enough in the week that I suspect Lars is thinking about what establishments to visit this weekend. 😉

Why JIRA should use Neo4j by Pieter-Jan Van Aeken.

From the post:

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 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.

This caught my eye because I have been in discussions about an upgrade from an older version of JIRA to the latest and greatest.

It’s not every feature but enough to convey the flavor of a possible graph mapping.

Given the openness of a graph model, does this suggest a model for mocking up topic map models?

I first saw this in a tweet by Peter Neubauer.

IKEA wardrobes and Graphs: a perfect fit! by Rik Van Bruggen.

The idea for this blogpost was quite long in the making. We all know IKEA which is, like Neo4j, from Sweden. Most of us have delivered a daring attempt at assembling one of their furnitures. And most recently, even my 8- and 10-year old kids assembled their Swedish bedside tables themselves. Win!

In the past year or so, ever so often does someone approached me to talk about how to use Neo4j in a manufacturing context. And every single time I thought to myself: what a great, wonderful fit! We all know “reality is a graph”, but when you look at manufacturing processes – and the way different process components interact – you quickly see that these wonderful flowchart diagrams, actually represent a network. A graph. And when you then start thinking about all the required parts and components that are required to deliver these processes – then it becomes even more clearer: the “bill of material” of manufactured goods can also, predictably, be represented as a graph.

So there you have it. Manufacturing processes and bills or materials can be represented as a graph. And IKEA cupboards, wardrobes, tables, beds, stools – everywhere. How to make the match?

Rik does a great job of demonstrating the use of Neo4j for the familiar task of assembling furniture. And suggests that a similar graph could be used by the manufacturer of such products.

I think it is implied that creating the graph for components and the assembly process is also a way to delay the onset of assembly itself.

Rik’s mention of Sweden, however, is a tip-off this example is culturally bound. To Sweden that is.

A graph of assembly instructions in the United States, particularly during the holiday season, would be substantially different than Rik’s.

Tracking the assembly instructions, the graph would follow these rules:

1. Number of nodes must not match number of parts
2. Labels on nodes might match part names and/or differ by one letter.
3. Labels on arcs would be correct no more than 80% of the time.
4. Assembly arcs would include arcs for other models.

Perhaps a new holiday tradition?

Creating an assembly graph for a randomly chosen set of instructions?

😉

Neo4j 2.0.0-M05 released by Peter Neubauer.

From the post:

We are proud to release Neo4j 2.0.0-M05 as a milestone today. The 2.0 project is now in full speed development after summer vacation. We’re getting close to feature completeness now, and we want to get this release out to you so you can give us refined feedback for the final release.

Peter covers the following highlights:

• Unique Constraints
• Label store
• AutoClosable transactions
• Minimalistic Cypher and JSON
• Deprecated > /dev/null

I’m not real sure what Peter means by “…summer vacation…,” must be one of those old European traditions. 😉

However, whatever that may mean, Neo4j 2.0.0-M05 does look like a must have release!

Graphity Server for social activity streams released (GPLv3) by René Pickhardt.

From the post:

It is almost 2 years over since I published my first ideas and works on graphity which is nowadays a collection of algorithms to support efficient storage and retrieval of more than 10k social activity streams per second. You know the typical application of twitter, facebook and co. Retrieve the most current status updates from your circle of friends.

Today I proudly present the first version of the Graphity News Stream Server. Big thanks to Sebastian Schlicht who worked for me implementing most of the Servlet and did an amazing job! The Graphity Server is a neo4j powered servlet with the following properties:

• Response times for requests are usually less than 10 milliseconds (+network i/o e.g. TCP round trips coming from HTTP)
• The Graphity News Stream Server is a free open source software (GPLv3) and hosted in the metalcon git repository. (Please also use the bug tracker there to submit bugs and feature requests)
• It is running two Graphity algorithms: One is read optimized and the other one is write optimized, if you expect your application to have more write than read requests.
• The server comes with an REST API which makes it easy to hang in the server in whatever application you have.
• The server’s response also follows the activitystrea.ms format so out of the box there are a large amount of clients available to render the response of the server.
• The server ships together with unit tests and extensive documentation especially of the news stream server protocol (NSSP) which specifies how to talk to the server. The server can currently handle about 100 write requests in medium size (about a million nodes) networks. I do not recommend to use this server if you expect your user base to grow beyond 10 Mio. users (though we are working to get the server scaling) This is mostly due to the fact that our data base right now won’t really scale beyond one machine and some internal stuff has to be handled synchronized.

Koding.com is currently thinking to implement Graphity like algorithms to power their activity streams. It was for Richard from their team who pointed out in a very fruitfull discussion how to avoid the neo4j limit of 2^15 = 32768 relationship types by using an overlay network. So his ideas of an overlay network have been implemented in the read optimized graphity algorithm. Big thanks to him!

Now I am relly excited to see what kind of applications you will build when using Graphity.

If you’ll use graphity

Please tell me if you start using Graphity, that would be awesome to know and I will most certainly include you to a list of testimonials.

By they way if you want to help spreading the server (which is also good for you since more developer using it means higher chance to get newer versions) you can vote up my answer in stack overflow:

http://stackoverflow.com/questions/202198/whats-the-best-manner-of-implementing-a-social-activity-stream/13171306#13171306

This is very cool!

Take Graphity for a spin and let René know what you think.

Perhaps we can all hide in digital chaff? 😉

Neo4j 1.9.3 now available by Ian Robinson.

From the post:

Today we’re pleased to release Neo4j 1.9.3. This is a bugfix release in the 1.9.x series and has no new features (though it does restore an old way of registering unmanaged extensions with the server).

If you’re on an earlier 1.9 release then you’re strongly encouraged to upgrade (which can be performed without downtime in an HA cluster). You can download it from the neo4j.org web site.

The Fall season of point releases approaches! 😉

Summer Release – Neo4j 2.0 Milestone 4 by Chris Leishman.

From the post:

Perfectly suited for your summer holiday exploration, we are proud to present the Neo4j 2.0 Milestone 4 release (2.0.0-M04).

In working towards a major 2.0 release with some outstanding new functionality, this milestone contains many beneficial and necessary changes, some of which will require changes in the way Neo4j is used (see: deprecations).

As Chris says, it is something to explore over the summer holidays!

Download.

BTW, update to Java 7 before you try this milestone. It’s required.

Neo4j 1.9.2 now available! by Jim Webber.

Jim announces that Neo4j 1.9.2 is available for download.

From the post:

Neo4j 1.9.2 is available immediately and is an easy upgrade from any other 1.9.x versions as there are no store upgrades required and so everyone on Neo4j 1.9 and Neo4j 1.9.1 is strongly encouraged to upgrade to 1.9.2.

You need to look at the release notes for 1.9.2:

The 1.9.2 release of Neo4j is a maintenance release that corrects a serious issue concurrency issue introduced in Neo4j 1.9.1, and resolves some other critical issues when reading from the underlying store. All Neo4j users are highly encouraged to upgrade to this version.

Any release that fixes “critical” errors is a must upgrade.

A Little Graph Theory for the Busy Developer by Jim Webber.

From the description:

In this talk we’ll explore powerful analytic techniques for graph data. Firstly we’ll discover some of the innate properties of (social) graphs from fields like anthropology and sociology. By understanding the forces and tensions within the graph structure and applying some graph theory, we’ll be able to predict how the graph will evolve over time. To test just how powerful and accurate graph theory is, we’ll also be able to (retrospectively) predict World War 1 based on a social graph and a few simple mechanical rules.

A presentation for NoSQL Now!, August 20-22, 2013, San Jose, California.

I would appreciate your asking Jim to predict the next major act of terrorism using Neo4j.

If he can predict WWI with “a few mechanical rules,” the “power and accuracy of graphs” should support prediction of terrorism.

Yes?

If you read the 9/11 Commission Report (pdf), you too can predict 9/11, in retrospect.

Without any database at all.

Don’t get me wrong, I really like graph databases. And they have a number of useful features.

Why not sell graph databases based on technical merit?

As opposed to carny sideshow claims?

Working examples for the ‘Graph Databases’ book by Joerg Baach.

From the post:

The examples in the ‘Graph Databases’ book don’t work out of the box. I’ve modified them, so that they do work (for chapter 3, that is).

In the version I have, 2013-02-25, the examples in question occur in chapter 4.

But whichever chapter, the corrections are welcome news.

BTW, there are other chapters that probably need the same treatment.

The Last Mile by Max De Marzi.

From the post:

The “last mile” is a term used in the telecommunications industry that refers to delivering connectivity to the customers that will actually be using the system. In the sense of Graph Databases, it refers to how well the end user can extract value and insight from the graph. We’ve already seen an example of this concept with Graph Search, allowing a user to express their requests in natural language. Today we’ll see another example. We’ll be taking advantage of the features of Neo4j 2.0 to make this work, so be sure to have read the previous post on the matter.

We’re going to be using VisualSearch.js made by Samuel Clay of NewsBlur. VisualSearch.js enhances ordinary search boxes with the ability to autocomplete faceted search queries. It is quite easy to customize and there is an annotated walkthrough of the options available. You can see what it does in the image below, or click it to try their demo.

Graphs are ok.

Storing data in graphs is better.

Useful retrieval of data from graphs is the best. 😉

Max does his usual excellent job of illustrating useful retrieval of information from a Neo4j graph.

His use of labels does remind me of a post I need to finishj.