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.
Mayank Gupta, SVP of Data and Wren Chan, VP of Foundational Architecture and Innovation from LPL Financial present how they use GraphAware Hume and Neo4j to power financial chat bots.
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.
This session features Dr. Alessandro Negro, noted graph database author and Chief Scientist at GraphAware, along with Patrick Wall, Director of Product Marketing at Neo4j. During this webinar, GraphAware explores the powerful scalability features of Neo4j 4.0 in a live demo using the COVID-19 Open Research Dataset.
In this five-minute interview (conducted at GraphTour NYC 2019), Neo4j caught up with Michal and spoke with him about everything from Neo4j 4.0 to funky use cases.
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.
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.
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.
