‘The amount of savings in time and effort [the search optimization] can deliver for our home offices, for our customers, is incredible.’
--Mayank Gupta, SVP for data, LPL Financial
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.
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.
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.
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 !
Watch a talk by Eric Wespi from Boston Scientific and GraphAware’s Eric Spiegelberg given at GraphConnect NY 2018.
In this talk, Luanne talks about ways how to use graphs in order to reduce chaos while delivering complex projects. Streamlining dependencies by promoting zero waste.
Real-Time Recommendations with Graphs and the Future of Search: Michal Bachman, Managing Director, GraphAware. Michal talks about how they use Neo4j in combination with Elasticsearch to power real-time recommendations.