Phil Cannata

Principal Enablement Specialist

As a Principal Enablement Specialist in the Revenue Enablement organization, I'm responsible for needs analysis, design, development, deployment and delivery of global sales, presales technical and implementation level training for our Oracle field sales and partner communities.

Areas of focus include:
- Data Management
- Artificial Intelligence
- Observability and Enterprise Management

I received a Ph.D. in 1980 from the University of Notre Dame in High Energy Particle Physics and have worked in the Computer Science industry for over 43 years starting with doing Unix development at Bell Laboratories in Murray Hill, NJ in the early 80s. My most significant contribution to Unix was the design and implementation of Shared Memory, Semaphores, and Memory Mapped Files in Unix 4.2 and Unix 5.0. I was a Research Director at MCC in Austin, Texas and then worked at IBM and Sun Microsystems. I have also been an Adjunct Professor at the University of Texas, Austin for 18 years teaching "Data Management", "Data Visualization”, “Data Analytics”, "Programming Languages", "Data Structures and Algorithms in Java and Python", Software Design, and "Networking". I authored two on-line textbooks for his courses at UT, one on "Semantic Data Management" and the other on "Data Visualization". I'm also an Oracle Certified Professional and taught "Oracle Database 12c: Data Mining Techniques" for Oracle University and "Data Analytics", and "SQL" classes at General Assembly in Austin, TX. I have publish four books on Amazon with novel approaches to teaching quantum mechanics and quantum field theory. I also enjoy bioinformatics and I'm a professional keyboard musician.

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Recent Blogs

Using JavaScript and LangChain for Retrieval Augmented Generation (RAG) with Oracle Graph

With the easy-to-use graph features in Oracle Database it is very easy to add graph to your RAG based apps. Here is an example with JavaScript and LangChain.

GenAI RAG Likes Explicit Relationships: Use Graphs!

Generative AI is only as good as the data it is trained on. How can additional data be added? This is where RAG comes in, RAG can help generate better output. Graphs provide additional context to RAG because they can model relationships between concepts in any given domain. Let us look at the basics of graphs in this blog and how they can help genAI.

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