erDiagram
    PERSON_DATA ||--o{ PERSON_ASSIGNMENT_DATA : "has assignments"
    PERSON_DATA ||--o{ PERSON_VOTE_DATA : "casts votes"
    PERSON_DATA ||--o{ PERSON_DETAIL_DATA : "has details"
    PERSON_DATA ||--o{ AI_PERSON_ANALYSIS : "has AI analysis"
    PERSON_DATA {
        string person_id PK
        string first_name
        string last_name
        string party
        string gender
        date born_year
        string status
        string image_url_192
    }

    AI_PERSON_ANALYSIS {
        bigint id PK
        string person_id FK
        string analysis_type
        float risk_score
        float activity_score
        text ai_summary
        float sentiment_score
        string model_version
        timestamp analyzed_at
        float confidence
    }

    COMMITTEE_PROPOSAL_DATA ||--o{ AI_DOCUMENT_ANALYSIS : "has AI analysis"
    COMMITTEE_PROPOSAL_DATA {
        bigint id PK
        string rm
        string hangar_id
        string committee
        string header
        text decision_type
    }

    AI_DOCUMENT_ANALYSIS {
        bigint id PK
        string document_id FK
        text ai_summary
        text key_topics
        float sentiment_score
        text impact_assessment
        string model_version
        timestamp analyzed_at
        float confidence
    }

    VOTE_DATA ||--o{ VOTE_DATA_EMBEDDED_ID : "identified by"
    VOTE_DATA ||--o{ AI_VOTING_PATTERN : "has pattern analysis"
    VOTE_DATA {
        bigint id PK
        string rm
        string issue
        string concern
        int total_votes
        int yes_votes
        int no_votes
        int abstain_votes
        int absent_votes
    }

    AI_VOTING_PATTERN {
        bigint id PK
        string vote_id FK
        text pattern_description
        float cohesion_score
        text anomaly_flags
        text cross_party_alignment
        string model_version
        timestamp analyzed_at
    }

    DOCUMENT_CONTENT_DATA ||--o{ AI_TEXT_EMBEDDING : "has embeddings"
    DOCUMENT_CONTENT_DATA {
        bigint id PK
        string doc_id
        text content
        string content_type
    }

    AI_TEXT_EMBEDDING {
        bigint id PK
        string document_id FK
        string embedding_model
        text embedding_vector
        int dimensions
        timestamp created_at
    }

erDiagram
    POLITICAL_ENTITY ||--o{ ENTITY_RELATIONSHIP : "has relationships"
    POLITICAL_ENTITY ||--o{ TEMPORAL_EVENT : "participates in"
    POLITICAL_ENTITY ||--o{ INFLUENCE_SCORE : "has influence"
    POLITICAL_ENTITY {
        string entity_id PK
        string entity_type
        string name
        jsonb metadata
        timestamp first_seen
        timestamp last_updated
    }

    ENTITY_RELATIONSHIP {
        bigint id PK
        string source_entity FK
        string target_entity FK
        string relationship_type
        float strength
        timestamp start_date
        timestamp end_date
        jsonb evidence
    }

    TEMPORAL_EVENT {
        bigint id PK
        string event_type
        timestamp event_date
        text description
        jsonb entities_involved
        float significance_score
        text ai_analysis
    }

    INFLUENCE_SCORE {
        bigint id PK
        string entity_id FK
        string influence_domain
        float score
        text methodology
        timestamp calculated_at
        string model_version
    }

    CROSS_NATIONAL_DATA {
        bigint id PK
        string country_code
        string parliament_id
        string data_type
        jsonb political_data
        timestamp collected_at
        string source_api
    }

    POLICY_IMPACT_MODEL {
        bigint id PK
        string policy_id FK
        text simulation_parameters
        jsonb predicted_outcomes
        float confidence_interval
        string model_version
        timestamp simulated_at
    }

graph TD
    subgraph "Self-Managing Data Layer (2030+)"
        A[AI Schema Optimizer] --> B[Adaptive Indexing Engine]
        B --> C[Automated Materialized View Manager]
        C --> D[Intelligent Data Lifecycle Controller]
        D --> E[Predictive Data Prefetcher]
    end

    subgraph "Global Political Data Fabric"
        F[Swedish Parliament Data] --> G[Federated Political Data Bus]
        H[Nordic Parliament Data] --> G
        I[EU Parliament Data] --> G
        J[Global Democratic Data] --> G
    end

    subgraph "AI-Enhanced Storage"
        K[Vector Store - Embeddings & Similarity]
        L[Graph Store - Relationships & Networks]
        M[Time-Series Store - Political Trends]
        N[Document Store - Full-Text & Media]
    end

    G --> K
    G --> L
    G --> M
    G --> N

    A --> K
    A --> L
    A --> M
    A --> N

    classDef ai fill:#9C27B0,stroke:#333,stroke-width:1px,color:white
    classDef data fill:#2196F3,stroke:#333,stroke-width:1px,color:white
    classDef storage fill:#4CAF50,stroke:#333,stroke-width:1px,color:white

    class A,B,C,D,E ai
    class F,G,H,I,J data
    class K,L,M,N storage

graph TD
    subgraph "AGI-Managed Knowledge Base (2034–2037)"
        A[Autonomous Knowledge Discovery] --> B[Self-Evolving Political Ontology]
        B --> C[Multi-Dimensional Relationship Engine]
        C --> D[Continuous Knowledge Synthesis]
        D --> E[Verified Knowledge Distribution]
    end

    subgraph "Verification & Trust"
        F[Cryptographic Provenance Chain]
        G[Bias Detection & Correction]
        H[Source Credibility Scoring]
        I[Human Oversight Interface]
    end

    subgraph "Global Democratic Knowledge Network"
        J[Federated Knowledge Nodes - Per Nation]
        K[Cross-Border Insight Exchange]
        L[Universal Democratic Health Metrics]
    end

    A --> F
    D --> G
    E --> H
    H --> I

    E --> J
    J --> K
    K --> L

    classDef agi fill:#E91E63,stroke:#333,stroke-width:1px,color:white
    classDef trust fill:#FF9800,stroke:#333,stroke-width:1px,color:white
    classDef global fill:#00BCD4,stroke:#333,stroke-width:1px,color:white

    class A,B,C,D,E agi
    class F,G,H,I trust
    class J,K,L global

timeline
    title CIA Data Model Evolution: 2026–2037

    section 2026 — AI-Enhanced Tables
      AI analysis tables for politicians and documents : ai_person_analysis, ai_document_analysis
      Vector embeddings for semantic search : pgvector extension, ai_text_embedding
      AI model audit trail : ai_model_audit tracking
      Enhanced materialized views with AI data : view_ai_enhanced_politician_summary

    section 2027–2028 — Knowledge Graph
      Political entity knowledge graph : entity_relationship with typed edges
      Temporal event streams : Real-time political event capture
      Cross-national data schema : Nordic + EU parliament data models
      AI reasoning chain storage : Full chain-of-thought audit tables

    section 2029–2030 — Intelligent Data Fabric
      Policy impact simulation storage : predicted outcomes + confidence
      Self-describing metadata : AI-maintained data dictionary
      Autonomous data quality monitoring : Agent-managed data integrity
      Federated data exchange protocols : International political data bus

    section 2031–2033 — Proto-AGI Data Management
      Self-optimizing schema : AI-driven schema evolution
      Causal relationship models : Decision-to-outcome linkages
      Predictive data prefetching : AI-anticipated data needs
      Multi-model data platform : Vector + graph + time-series + document stores

    section 2034–2037 — AGI Knowledge Base
      Self-evolving political ontology : Autonomous concept discovery
      Living political knowledge base : Complete democratic memory
      Verified knowledge distribution : Cryptographic provenance + bias detection
      Global democratic knowledge network : Federated transparency infrastructure