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Reaching the “Single Brain” with Hume
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Resources - graphs
Videos, Slides, Case Studies and other GraphAware related resources
Graph-Powered Machine Learning Q&A
An introduction to Graph-Powered Machine Learning written by our very own Dr. Alessandro Negro. This book is an extraction of 60 combined years of experience in graphs, and explains how graphs and graph databases can serve machine learning projects.
10+ Entity States in Graph Visualisation and How To Make The Best Of Them
Dr. Miro Marchi and Michal Trnka explore 10 of the most useful graph entity states using Cypher to enrich entities with contextual information enabling powerful interactions.
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.
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.
It Depends (and why it’s the most frequent answer to modelling questions)
The answer to most general purpose graph modelling questions is “it depends”. This talk demonstrates the pitfalls of modelling without knowing use cases- it shows how two sets of people can produce two different models for the same set of data elements, and how use cases should guide the model.
Fix your microservice architecture using graph analysis
So, for your brand new project, you decided to throw away your monolith and go for microservices. But after a while, you realize things are not going as smoothly as expected ;-)
Hopefully, a graph can help to detect antipatterns, visualize your whole system, and even do cross-service impact analysis.
In this talk, we’ll analyze a microservice system based on Spring Cloud, with jQAssistant and Neo4j. We will see how it can be helpful to answer questions like:
do I have anti-patterns in my microservice architecture ?
which services / applications are impacted when doing a database refactoring ?
is my API documentation / specification up to date ?
how to get an up to date visualization of my whole system ?
and more !
Lean Dependency Management with graphs
Unblocking dependencies benefits any organization that performs work concurrently. Dependencies are connected and modelling them as a graph surfaces those connections quickly, enabling decisions to be taken that promote zero waste and more efficient delivery.
How to Know What You Know: 5-Minute Interview with Dr. Alessandro Negro, Chief Scientist at GraphAware
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.
How Boston Scientific Improves Manufacturing Quality Using Graph Analytics
Watch a talk by Eric Wespi from Boston Scientific and GraphAware’s Eric Spiegelberg given at GraphConnect NY 2018.