Technical resources for developers learning Unified Model Theory (UMT) and converged data modeling on Oracle AI Database 26ai — model the domain once, project the access for every consumer.
This hub collects hands-on, runnable labs and companion material for the UMT / converged-database content series. Nothing here is slideware: every lab boots a real Oracle AI Database 26ai container and proves its claims by executing them — document, relational, graph, vector, spatial, and full-text in one engine, one transaction, one optimizer.
One truth. Many shapes. Every claim runs.
This repository is organized so that each runnable asset lives in its own folder under a category. More labs and companion material will be added as the series grows.
Self-contained, runnable labs that validate converged-model claims against a live Oracle AI Database 26ai. Each lab includes a Docker environment, a validator, and its own README and quickstart, and runs itself nightly in CI so the proofs stay honest.
| Name | Description | Link |
|---|---|---|
| converged-database-lab | Runnable proofs for the converged-database article series — JSON Relational Duality, single-table vs. converged modeling, graph, vector, spatial, and full-text claims, each executing against a free Oracle AI Database 26ai container. |  |
More labs coming as the content series expands.
Each lab is self-contained. To run the converged-database lab:
cd labs/converged-database-lab
docker compose up -d --build oracle # ~2 GB image; first boot seeds the domain (~2 min)
pip install -r validator/requirements.txt
python validator/run.py # runs every module, prints ASSERT resultsSee the lab's own README for details, troubleshooting, and the module map.
Relational and document are not competing paradigms — they are orthogonal projections of the same truth. UMT models the domain once as a normalized canonical form, then projects whatever shape each consumer needs — a document, a graph, a vector, a relational result — from that single source, with no copies to drift out of sync. On a converged engine, the access pattern still drives the model; you just stop walking through one-way doors to serve it.
This project welcomes contributions from the community. Before submitting a pull request, please review our contribution guide.
Please consult the security guide for our responsible security vulnerability disclosure process.
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