Graph Databases and Artificial Intelligence Webinar Video

Graph Databases and Artificial Intelligence Webinar Video

Previously Recorded Academic Webinar Series

Graph Databases and A.I.

Graph databases are the fastest growing type of databases today.  Although their flexibility has always been strong, now they have scale-out ability to cost-effectively store every touchpoint of our interactions with our customers including down to the individual slide they view?  Now organizations are using this knowledge to help guide our customers to the right courses and to adapt the learning experiences to their goals.  We will show how our graph databases are the next step in bringing AI into the classroom.

Take Aways:

  1. What is a graph database?

  2. Why are they becoming popular?

  3. How do they help us quickly create and view a single view of our customers and our students?

  4. How do they work with predictive analytics and AI?

  5. How can we create better recommendation systems about what course to take, what lesson to start with or what slide to view?


Dan McCreary is an author, speaker, and evangelist for graph technologies. As a Distinguished   Engineer at Optum, he works with business units within United Healthcare (Fortune 6) to evaluate and   integrate advanced technologies including AI, Graph, and NoSQL into their digital ecosystem. Dan has a   solid background and understanding of solution architecture. He has worked for Steve Jobs at NeXT   Computer, at Bell Labs as an integrated circuit designer and as the owner of Integrity Solutions, a   consulting firm of over 70 employees. He has expertise in NLP, US federal data integration (NIEM), XML  standards, taxonomy and ontology development, and semantic search.


You are free to use any of the material I presented in any of your classrooms as long as you don’t charge for them.  Attribution is always appreciated!  My only request is please don’t include these works in any commercial works that are sold for a profit without permission.

  • Attribution – Noncommercial – ShareAlike 3.0 (CC BY-NC-SA 3.0)
  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • NonCommercial — You may not use the material for commercial purposes.
  • ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

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