Semantic digital twin for geofence anomaly detection
Overview
Fleet telemetry tells you where a vehicle is. It rarely tells you whether that's normal. This project builds a semantic digital twin over GPS trajectory data: a hybrid model predicts where a vehicle should be next and scores deviations, while a SAREF/RDF knowledge graph makes every observation, zone, and anomaly queryable in SPARQL.
The bet: anomaly detection gets dramatically more useful when its outputs live in a knowledge graph instead of a log file — because then the question "which vehicles crossed which zones abnormally, and when" is one query, not a data-engineering project.
The system
A batch job enriches raw trajectories into typed observations and writes them as RDF triples; the endpoint serves the hybrid prediction/scoring model. Everything runs as containers on Nebius serverless — the whole pipeline is reproducible from a clean account.
Results
How it was built
Built end-to-end with my spec-driven agentic framework: strategist / implementer / operator roles, per-module guardrail files, adversarial read-only verification of every change, and a live run as the definition of done.
Read the framework →Changelog
The lesson
Digital twins earn their keep at the semantic layer. The model finds the anomaly; the graph makes it answerable. Most telemetry projects stop one layer too early.