During GraphConnect San Francisco 2015, we introduced the concept of Graph-Aided Search and released the first module providing Neo4j data replication to Elasticsearch.
In the Bersin Predictions for 2016 report, Josh Bersin states that “it feels as though everything in the world of talent is changing – from the way we recruit and attract people, as well as how we reward them, to the way we learn, and how we curate and manage our entire work-life experience”.
A great part of the world’s knowledge is stored using text in natural language, but using it in an effective way isstill a major challenge. Natural Language Processing (NLP) techniques provide the basis for harnessing this huge amountof data and converting it into a useful source of knowledge for further processing.
In our previous blog postwe introduced the concept of Graph Aided Search. It refers to a personalised user experience during search where theresults are customised for each user based on information gathered about them (likes, friends, clicks, buying history, etc.).This information is stored in a graph database and processed using machine learning and/or graph analysis algorithms.
At GraphAware, we live and breathe Neo4j. For three years, we have been helping customers around the world embrace thisamazing technology as a solution to many interesting problems. Mainstream applications of graphs, such as real-timerecommendations, fraud detection, impact analysis, and graph-aided search, have been getting a lot of media attention.
As of version 2.1, Neo4j OGM will support persistence events. Although a date for the release of 2.1 isn’t known at thetime of writing, we think this is an important and exciting new feature and so we’ll be writing a series of posts aboutit over the next few weeks to whet your appetites. In this first post we’ll take a quick tour of the new Events mechanismin the OGM, and provide some examples of how we might use it in our own applications. But first, some background…
Spring Data Neo4j 4.1 introduces the ability to map nodes and relationships returned by custom Cypher queries to domain entities. This blog post will explain how different types of query results map to entities.
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 rightand the implications of getting it wrong can be serious. In this blog post, we introduce the integration of Spring Securityinto Neo4j which provides important security controls and mechanisms for enterprises and governments that make use of theworld’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 extensionthat addresses it. The latest addition to our product portfolio, introduced in this post, is a simple library that automaticallyexpires data from the Neo4j graph database.
Our previous article demonstrated how easy it was to build an application using Spring Data Neo4j 4.