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Securing Neo4j with GraphAware Enterprise

29 Mar 2016 by Michal Bachman Neo4j GraphAware Intermediate Security Enterprise

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.

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Expiring Data in Neo4j

15 Mar 2016 by Michal Bachman Neo4j GraphAware Intermediate

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.

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Processing Large Sets of Nodes with Cypher

10 Dec 2015 by Christophe Willemsen Neo4j Cypher

Iterating over large numbers of nodes using Cypher is quite a common use case in Neo4j. Typically, the reason for doing this is that we want to perform some kind of operation for each one of these nodes. In this blog post, we will use one million TestNodes and try to iterate over them in order to index their contents into a freshly created Elasticsearch index. There are three approaches we can take, two of which are quite common, but the most performant technique is largely unknown.

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Faster Recommendations with Neo4j 2.3 Triadic Selection

20 Oct 2015 by Alessandro Negro & Christophe Willemsen Neo4j Cypher Recommendations

Recently, Neo Technology announced the 2.3.0-RC1 release of their Neo4j graph database. One of the key new features is Triadic Selection built into Cypher’s Cost Based Planner. In this blog post, we will explore the Triadic Selection in detail and demonstrate how significantly it can speed up recommendations computed in Neo4j.

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Recommendations with Neo4j and Graph-Aided Search

30 Sep 2015 by Michal Bachman Neo4j Recommendations Search Elasticsearch

For the last couple of years, Neo4j has been increasingly popular as the technology of choice for people building real-time recommendation engines. Having been at the forefront of the graph movement through client engagements and open-source software development, we have identified the next step in the natural evolution of graph-based recommendation engines. We call it Graph-Aided Search.

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