With the aim to monitor, prevent, and predict cyber attacks on various systems and infrastructures, the cyber defence company needed a solution to ingest and connect all available data and discover threat patterns.
In this presentation, you’ll learn how companies are building Knowledge Graphs with Neo4j and the Hume platform in order to surface previously undiscoverable insights. We’ll go over the process of analysing unstructured data using Machine Learning techniques and how graphs are a wonderful representation for storing Knowledge, making it naturally connectable. Lastly, a Graph Visualisation demonstration will take place, showing new insights discovered from the results of the previous operations.
Interview with Kyle McNamara, CEO, Americas at GraphAware, about how GraphAware works alongside Neo4j (conducted at GraphTour DC 2019)
In this informative talk, Christophe Willemsen, CTO at GraphAware, provides valuable insights on how to use Neo4j’s Lucene-based search engine to achieve relevant search results. With Willemsen’s expert guidance, you’ll learn useful tips and tricks for optimizing your search queries and improving the accuracy of your search results. As the CTO of GraphAware, a company specializing in graph database technology, Willemsen is well-versed in the capabilities of Neo4j and is well-equipped to share his knowledge with you.
Christophe Willemsen, CTO at GraphAware, goes over some tips and tricks on Relevant Search with Neo4j’s Lucene based search engine.
Are you interested in learning about how machine learning can be leveraged to build a knowledge graph, enabling businesses to differentiate themselves and thrive in today’s competitive marketplace? In this talk, we’ll show you how computer vision, natural language processing and understanding, knowledge enrichment, and graph-native algorithms can be combined to extract valuable insights from various unstructured data sources. Whether you’re a business owner looking to gain a competitive edge or a developer looking to expand your skillset, this talk is sure to be of interest to you.
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.
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.
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.
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 !
