With the aim to monitor, prevent, and predict cyber attacks on various systems and infrastructures, the cyber defence company needed a solution to ingest and connect all available data and discover threat patterns.
SDN is a Spring Data project for Neo4j. It uses Neo4j-OGM under the hood (very much like Spring Data JPA uses JPA) and provides functionality known from the Spring Data world like repositories, derived finders or auditing. Neo4j recently released Spring Data 2.0 (Kay) / Spring Data Neo4j 5.0 and in this session we’ll show some of the new cool features. This release contains support for dynamic properties, schema based loading, field access only, and more.
Graphgen aims at helping people prototyping a graph database, by providing a visual tool that ease the generation of nodes and relationships with a Cypher DSL. Many people struggle with not only creating a good graph model of their domain but also with creating sensible example data to test hypotheses or use-cases. Graphgen aims at helping people with no time but a good enough understanding of their domain model, by providing a visual dsl for data model generation which borrows heavily on Neo4j Cypher graph query language. The ascii art allows even non-technical users to write and read model descriptions/configurations as concise as plain english but formal enough to be parseable. The underlying generator combines the DSL inputs (structure, cardinalities and amount-ranges) and combines them with a comprehensive fake data generation library to create real-world-like datasets of medium/arbitrary size and complexity. Users can create their own models combining the basic building blocks of the dsl and share their data-descriptions with others with a simple link.
Vince Bickers, Principal Consultant at GraphAware and main contributor to Spring Data Neo4j, gives an update on the release of the new version of SDN.
In this talk, Luanne will share insights about the business value of knowledge graphs and their contribution to relevant search in an e-commerce domain for a Neo4j customer. With text search and catalog navigation being the entry point of users to the system and in fact, driving revenue, the talk will explain the challenges of relevant search and how graph models address them. Dr. Alessandro will then talk about various techniques used for information extraction and graph modelling. He will also demonstrate how to seamlessly introduce knowledge graphs into an existing infrastructure and integrate with other tools such as ElasticSearch, Kafka, Apache Spark, OpenNLP and Stanford NLP.
Here’s another great interview with a long time Graphista that has done a lot of really interesting work in our French community, and is now having lots of graph-fun at GraphAware: Nicolas Mervaillie.
Speaker: Dr. Alessandro Negro, Chief Scientist at GraphAware. September 20th 2017 at Westin Hotel, Abu Dhabi UAE.
Streamed live on Aug 3, 2017 Following on from the Introduction to Neo4j Bolt Drivers in this session we’ll be hosting a roundtable where Neo4j driver authors will be sharing their experiences.
During this talk, Christophe, Principal Consultant at GraphAware will walk you through the design of building Conversational Bots. To this end, he used Amazon Alexa and combined it with a Natural Language Processing stack backed by a Neo4j Graph Database.
You will discover the basics of an Amazon Alexa skill and how the user experience with voice devices can be enhanced with graph based algorithms such as recommendations.
Michael Hunger introduces Neo4j to the audience of the Czech GraphDB Meetup in Prague, Czech Republic
At GraphDB Meetup Czech Republic in Prague, Christophe Willemsen talks about creating a chatbot with Amazon Alexa, Neo4j and GraphAware NLP
