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.
At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs. Graph-Powered Machine Learning teaches you how to use graph-based algorithms and data organization strategies to develop superior machine learning applications.
Read the interview with our Chief Data Scientist Alessandro Negro published on Neo4j blog, where he talks about how GraphAware uses natural language processing to help companies gain a better understanding of the knowledge that is spread across their organization.
GraphAware is pleased to announce the release of “Neo4j : Déploiement”, a french book explaining how to use Neo4j in a real life project. The book is co-authored by Sylvain Roussy and Nicolas Rouyer along with our Senior Consultant Nicolas Mervaillie. You can get it from your favorite (french) bookstore or on D-Booker website.
GraphAware: Towards Online Analytical Processing in Graph Databases
MSc. Thesis submitted for MSc. program in Computing at Imperial College London, written by GraphAware's managing director Michal Bachman.
