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.
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.
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 !
Visualizing a complex graph is a task of graph simplification and providing well-thought visual cues, the best UI goes unnoticed. This talk will summarize current approaches and present a novel user interaction pattern, which takes advantage of a performant Neo4j graph engine.
Visualizing a complex graph is a task of graph simplification and providing well-thought visual cues, the best UI goes unnoticed. This talk will summarize current approaches and present a novel user interaction pattern, which takes advantage of a performant Neo4j graph engine.
Alessandro Negro, Chief Scientist at GraphAware, delivers a presentation called Using Knowledge Graphs to predict customer needs, improve product quality and save costs during the SmartData Summit 2019 in Dubai.
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.
Tracking end of line manufacturing issues to their source can be a daunting task. Boston Scientific, in partnership with GraphAware, has used the Neo4j platform to build a manufacturing quality tool that offers dramatic improvements to the time, quality, and quantity of investigations. In this talk we will review a manufacturing value stream in a graph and discuss the analysis methods available, which result in striking increases in business efficiencies, for this unique application. We will also present how the system was implemented within the existing data architecture and then scaled from a laptop investigational tool to an enterprise-grade solution with Neo4j Server.
When privacy matters! A series of challenges for chatbots in data-sensitive businesses such as healthcare and finance by Christophe Willemsen
Meetup: Integration of Chatbots in Healthcare and BFSI, Dubai, 1.11.2018
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.
The GraphAware Audit module seamlessly and transparently captures a full audit history who, when, and how a graph was modified.
