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Graph Hyper-Growth Ahead: 5-Minute Interview with Kyle McNamara

Interview with Kyle McNamara, CEO, Americas at GraphAware, about how GraphAware works alongside Neo4j (conducted at GraphTour DC 2019)

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To Be or Not To Be, Neo4j Full Text Search Tips and Tricks

Christophe Willemsen, CTO at GraphAware, goes over some tips and tricks on Relevant Search with Neo4j’s Lucene based search engine.

To Be or Not To Be, Neo4j Full Text Search Tips and Tricks

Christophe Willemsen, CTO at GraphAware, goes over some tips and tricks on Relevant Search with Neo4j’s Lucene based search engine.

Social media monitoring with ML-powered Knowledge Graph

Ever wondered how ML can be used to build a Knowledge Graph to allow businesses to successfully differentiate and compete today? We will demonstrate how Computer Vision, NLP/U, knowledge enrichment and graph-native algorithms fit together to build powerful insights from various unstructured data sources.

Social media monitoring with ML-powered Knowledge Graph

Ever wondered how ML can be used to build a Knowledge Graph to allow businesses to successfully differentiate and compete today? We will demonstrate how Computer Vision, NLP/U, knowledge enrichment and graph-native algorithms fit together to build powerful insights from various unstructured data sources.

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.

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 !

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 !

Challenges in knowledge graph visualization

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.