Over 10,000 physical typewritten documents from 1932 to 1941 had to be digitised, structured, and connected in order to create a single, centralised source of knowledge, for enabling the analysis of historical processes.
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.
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.
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.
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.
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.
To improve the performance of your microservice architecture, you may consider using graph analysis techniques. By using tools like jQAssistant and Neo4j, you can identify potential issues, better understand the relationships between different services, and even analyze the potential impact of changes on your system. With these tools, you can answer questions like:
Are there any antipatterns present in my microservice architecture? How will certain database refactoring efforts affect the other services in my system? Is my API documentation and specification accurate and up to date? Can I get a clear and current visualization of my entire system?
By implementing graph analysis techniques, you can work towards optimizing the design and functionality of your microservice architecture.
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.
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.
Watch a talk by Eric Wespi from Boston Scientific and GraphAware’s Eric Spiegelberg given at GraphConnect NY 2018.
