Bernt
754c89506b
- Layer 1: ArchiveArtifact — immutable original with retention policy - Layer 2: KnowledgeArtifact — extracted knowledge (observations, segmentations, feature vectors, relations) - Layer 3: Knowledge Graph / Ontology (documented, not implemented) - Layer 4: Decision Intelligence (existing DecisionCase) - DataLifecycle: tracks every step with artifact lineage - Key principle: AI models trained on curated datasets, not whole archive - Ontology answers 'what does it mean in our domain?' Long-term goal: Every observation converted once to structured knowledge, reused infinitely for analysis, decisions, training. Next: PR-005A — Minimal Mission Import UI for MVP-0
68 lines
2.1 KiB
Markdown
68 lines
2.1 KiB
Markdown
# ADR-011: Four-Layer Data Architecture
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## Status
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Accepted
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## Context
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We need a clear separation between raw data, extracted knowledge, and decision intelligence to avoid building expensive, tightly-coupled AI systems.
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## Decision
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Separate into four layers:
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### Layer 1: Raw Archive (cheap, immutable)
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- Original files never changed
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- Hash guarantees integrity
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- Storage policy: active (30 days) → archive (long-term, cheaper)
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### Layer 2: Knowledge Extraction
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- Objects, geometry, GPS, classifications, bounding boxes, embeddings
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- Observations and Evidence created
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- Structured knowledge used for most queries, not video files
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### Layer 3: Knowledge Graph / Ontology
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- Road A12 → has Observation → Crack → belongs to Area → Stockholm
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- Answers: "Show all main roads where cracking increased last 12 months"
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- Comes from knowledge graph, not by reading video files
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### Layer 4: Decision Intelligence
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- Verified Decision Cases
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- Curated datasets for AI training
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- Business impact tracking
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## Key Principles
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1. **Archive Artifact** = Original file, never modified
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2. **Knowledge Artifact** = Extracted knowledge, can be re-generated
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3. **Ontology before model** — taxonomy answers "what does it mean in our domain?"
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4. **AI models trained on curated datasets**, not whole archive
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## Data Lifecycle
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```
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Upload → Immutable Archive → Metadata Extraction → Knowledge Extraction
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→ Ontology Mapping → Decision Pipeline → Learning → Archive Retention
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```
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Each step produces a new Artifact with version history.
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## Long-term Goal
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"Every real observation is converted once to structured knowledge and can then be reused infinitely for analysis, decision support, history, training, and future AI models."
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## Consequences
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### Positive
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- Raw data = revisionable archive
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- Real product = growing knowledge base + verified Decision Cases
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- Can swap AI models without losing originals
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- Cheaper storage (archive vs active)
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### Negative
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- More complex pipeline
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- Need to manage extraction models
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- Knowledge graph maintenance
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## Related
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- ADR-002: Artifact is common base contract
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- ADR-003: Event Sourcing for traceability
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