What a year it’s been for all of us at GraphAware!
Find out what's new in the Neo4j world
What a year it’s been for all of us at GraphAware!
Last month, the 5th edition of GraphConnect San Francisco took place at the Hyatt Regency SF. It was the biggest graph technology event ever and GraphAware proudly contributed as a sponsor, with one main talk, two lightning talks and our GraphHero stand ｡^‿^｡ This edition’s big announcement was the upcoming new landmark release of Neo4j 3.1, “The database for the connected enterprise”, which introduces a new state-of-the-art clustering architecture and new security architecture to meet enterprise requirements for scale and security. There will be a lot to say about this release, but you can already try the beta release as we have done!
A great part of the world’s knowledge is stored using text in natural language, but using it in an effective way is still a major challenge. Natural Language Processing (NLP) techniques provide the basis for harnessing this huge amount of data and converting it into a useful source of knowledge for further processing.
For most organisations, data security is extremely important. The topic comes up every single time we are training, consulting, or otherwise engaging in the world of graphs and Neo4j. At the same time, security is very difficult and time-consuming to get right and the implications of getting it wrong can be serious. In this blog post, we introduce the integration of Spring Security into Neo4j which provides important security controls and mechanisms for enterprises and governments that make use of the world’s most popular graph database.
At GraphAware, we help organisations in a wide range of verticals solve problems with graphs. Once we come across a requirement or use case two or three different times, we typically create an open-source Neo4j extension that addresses it. The latest addition to our product portfolio, introduced in this post, is a simple library that automatically expires data from the Neo4j graph database.
GraphAware is very proud to sponsor GraphConnect Europe 2015, the only conference that focuses on the rapidly growing world of graph databases and applications that make sense of connected data. The conference takes place in London on 7th May 2015.
01 Apr 2015 by Luanne Misquitta GraphAware
Graph Aware Ltd. is excited to announce their new partnership with Glasses Inc. Managing Director Michal Bachman claims that wearers of GA-Glass become truly graph aware, allowing them to boldly go where no Glass has traversed before.
Specialist in Neo4j consultancy, training, and software development, Graph Aware Ltd has been selected as one of Neo Technology’s first UK solution partners, under its newly launched partnership program.
In this post, we’d like to introduce the first version of the GraphAware Neo4j ChangeFeed - a GraphAware Runtime Module that keeps track of changes made to the graph.
Modelling and querying time-based events in a graph is a fairly common discussion topic and a frequently asked question on Q/A sites. In this blog post, we evaluate some of the common approaches and introduce GraphAware TimeTree, a GraphAware Framework Module that simplifies modelling time and events in Neo4j.
In the first part of this short series about random graph models, we talked about why they are useful and had a brief look at two of them: Erdos-Renyi graphs and Barabasi-Albert model. In this post, we take a look at the “small world” phenomenon and another network model, namely the Watts-Strogatz model.
Efficient counting of relationships in Neo4j was the cornerstone of my Master Thesis and the reason the very first GraphAware Framework module called the Relationship Count Module was born. The improvements in Neo4j 2.1 around dense nodes and the addition of getDegree(…) methods on the Node interface made me eager to do some benchmarking around relationship counts again.
When one obtains a graph data from a measurement on a real world network, it is sometimes useful to make comparison with a random graph. Such graph is characterised by certain degree distribution, which you can imagine to be a list of degrees of nodes present in the network. The most interesting distributions have certain functional dependence which allows one to infer what processes are dominant in formation of the network. The processes consequently characterise the relationships between the nodes.
One of the main goals of the GraphAware Framework is to simplify and speed up development with Neo4j. Although it is called a “framework” for reasons explained elsewhere, today we will simply treat it as a library of useful, tested, and documented Java code. The feature we will introduce is called Improved Transaction Event API, which is exactly what it says on the tin.
A couple of days ago, I wrote about unit testing with GraphUnit. GraphUnit tested the state of an embedded Neo4j database. What if you run Neo4j in standalone server mode? Fortunately, you can still test it and match subgraphs using the GraphAware Neo4j RestTest library.
Today, it is exactly one year ago since Graph Aware Limited was incorporated. It started as a one man show, whilst I was finishing my MSc. Thesis at Imperial College London. Since then, we’ve been growing slowly but steadily and will be moving to our new London office fairly soon (announcements to come). We have happy clients in London, New York, Copenhagen, Barcelona, Prague, and Accra.
Recently, we announced the GraphAware Framework. Today, I would like to introduce its first feature called GraphUnit. GraphUnit is a component that helps Java developers unit test their code that talks to Neo4j and mutates data.
In this short blog post, I would like to introduce the GraphAware Neo4j Framework. Its goal is very ambitious: we’d like to make it as useful for Neo4j developers, as the Spring Framework is for Java developers. The Framework aims at speeding up development with Neo4j by providing a platform for building useful generic as well as domain-specific functionality, analytical capabilities, graph algorithms, and more.