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European Space Agency case study

Hume maps a segment of the space and satellite ecosystem for the European Space Agency

‘The ability to customise Hume Actions via Cypher queries provided ESA with flexibility to cover a range of use cases and customers.’

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Unparalleled Graph Database Scalability Delivered by Neo4j 4.0 - Graph Powered Machine Learning

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

Hume Contact Tracing Demo (COVID-19)

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.

Unparalleled Graph Database Scalability Delivered by Neo4j 4.0

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.

Neo4j 4.0 Security Rocks: Interview with Michal Bachman, CEO, GraphAware

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.

Graph-Powered Machine Learning Manning Book by Alessandro Negro, Chief Scientist at GraphAware

Graph-Powered Machine Learning - Book

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.

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Knowledge Graphs in Action

In this presentation, you’ll learn how companies are building Knowledge Graphs with Neo4j and the Hume platform in order to surface previously undiscoverable insights. We’ll go over the process of analysing unstructured data using Machine Learning techniques and how graphs are a wonderful representation for storing Knowledge, making it naturally connectable. Lastly, a Graph Visualisation demonstration will take place, showing new insights discovered from the results of the previous operations.