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.
This presentation by Christophe Willemsen, CTO, GraphAware, guides you through security best practices for Neo4j development.
Christophe Willemsen, CTO, GraphAware, explains how to apply NLP to extract entities and key phrases to build and search knowledge graphs
Mayank Gupta, SVP of Data and Wren Chan, VP of Foundational Architecture and Innovation from LPL Financial present how they use GraphAware Hume and Neo4j to power financial chat bots.
Using natural language processing, GraphAware's Hume software will extract words and phrases from COVID-19 data streams.
‘The ability to customise Hume Actions via Cypher queries provided ESA with flexibility to cover a range of use cases and customers.’
Presentation by Dr. Alessandro Negro, Chief Scientist at GraphAware and author of the Manning’s book Graph-powered machine learning, that covers the following topics:
Why unlimited scale is important when using graph databases
The new graph database scaling capabilities built by Neo4j developers
The role of graphs to support machine learning application
How Neo4j assists customers in scaling their applications
Concrete examples of machine learning projects that can leverage graph sharding
The recording is available as well: https://bit.ly/39ZqFVE
Demonstration of GraphAware Hume, a graph-powered insights engine. Shows how Hume can be applied to processing and analysing structured data to surface insights. The use case for this demo is coronavirus contact tracing and smart quarantine.
This session features Dr. Alessandro Negro, noted graph database author and Chief Scientist at GraphAware, along with Patrick Wall, Director of Product Marketing at Neo4j. During this webinar, GraphAware explores the powerful scalability features of Neo4j 4.0 in a live demo using the COVID-19 Open Research Dataset.
In this five-minute interview (conducted at GraphTour NYC 2019), Neo4j caught up with Michal and spoke with him about everything from Neo4j 4.0 to funky use cases.
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.