📊 EU Parliament Monitor — Future Data Model

🗄️ Enhanced European Parliament Data Architecture
🎯 From Static Files to Real-Time Multi-Parliament Intelligence (2026-2037)

📋 Document Owner: CEO | 📄 Version: 3.0 | 📅 Last Updated: 2026-03-19 (UTC)
🔄 Review Cycle: Quarterly | ⏰ Next Review: 2026-06-19
🏷️ Classification: Public (Open Source European Parliament Monitoring Platform)

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📚 Architecture Documentation Map

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🔐 ISMS Policy Alignment

This future data model is designed to implement all controls from Hack23 AB's ISMS framework as the EU Parliament Monitor platform evolves.

Related ISMS Policies

Policy Domain	Policy	Planned Implementation
🔐 Core Security	Information Security Policy	Overall security governance framework for enhanced monitoring
🛠️ Development	Secure Development Policy	Security-integrated development lifecycle enhancements
🌐 Network	Network Security Policy	CDN architecture, WAF, DDoS protection
🔒 Cryptography	Cryptography Policy	Content signing, TLS 1.3, integrity verification
🔑 Access Control	Access Control Policy	MCP authentication, request authorization
🏷️ Data Classification	Data Classification Policy	European Parliament data classification
🔍 Vulnerability	Vulnerability Management	Enhanced automated scanning and monitoring
🚨 Incident Response	Incident Response Plan	Automated incident detection and response
💾 Backup & Recovery	Backup Recovery Policy	Content backup, version control, recovery
🔄 Business Continuity	Business Continuity Plan	Multi-CDN deployment, disaster recovery
🤝 Third-Party	Third Party Management	CDN provider security assessment
🏷️ Classification	Classification Framework	Business impact analysis for platform

Compliance Framework Mapping

Framework	Version	Relevant Controls
ISO 27001	2022	A.5.1, A.8.25, A.8.26, A.8.27
NIST CSF	2.0	GV.OC, GV.RM, ID.AM, PR.AT
CIS Controls	v8.1	Control 1-5, 14, 16

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📋 Executive Summary

This document defines the evolution of EU Parliament Monitor's data model from a simple static file structure to a sophisticated multi-database architecture supporting real-time streaming, historical analysis, cross-parliament comparisons, and AI-powered intelligence.

Transformation Overview

Aspect	Current (2026)	Future (2027)	Benefit
Storage	Flat HTML files	PostgreSQL + MongoDB + Redis	Query flexibility, scalability
Structure	Unstructured HTML	Relational + Document + Graph	Rich relationships, analytics
Update Frequency	Daily batch	Real-time streaming	Sub-minute latency
Query Capability	None (static)	GraphQL + SQL + full-text	Complex queries, APIs
Historical Data	Overwrite	Temporal versioning	Trend analysis, auditing
Data Sources	EP only	28 parliaments	Comprehensive coverage
Intelligence	None	ML features, embeddings	AI-powered insights

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📅 Data Model Evolution Roadmap

gantt
    title Data Model Evolution Roadmap
    dateFormat YYYY-MM

    section Phase 1: Foundation
    Relational Schema Design           :p1a, 2026-04, 2M
    TimescaleDB Setup                  :p1b, 2026-05, 1M
    MongoDB Document Store             :p1c, 2026-05, 1M
    Redis Cache Layer                  :p1d, 2026-06, 1M

    section Phase 2: Intelligence
    ML Feature Store                   :p2a, 2026-07, 2M
    Vector Embeddings                  :p2b, 2026-08, 1M
    Graph Relationships                :p2c, 2026-09, 1M

    section Phase 3: Multi-Source
    National Parliament Schemas        :p3a, 2026-10, 3M
    Unified Data Model                 :p3b, 2026-11, 2M
    Cross-Parliament Links             :p3c, 2026-12, 1M

    section Phase 4: Optimization
    Query Performance Tuning           :p4a, 2027-01, 2M
    Archival Strategy                  :p4b, 2027-02, 1M
    Data Quality Monitoring            :p4c, 2027-03, 1M
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---

📐 Future Entity Relationship Diagram

Comprehensive view of all entities, relationships, and data flows across the future architecture.

erDiagram
    %% Core Entities
    ARTICLE ||--o{ ARTICLE_VERSION : "has versions"
    ARTICLE ||--o{ ARTICLE_TRANSLATION : "has translations"
    ARTICLE ||--o{ ARTICLE_SOURCE : "cites"
    ARTICLE }o--|| ARTICLE_TYPE : "belongs to"
    ARTICLE }o--|| GENERATION_JOB : "created by"
    ARTICLE ||--o{ QUALITY_SCORE : "assessed by"
    ARTICLE ||--o{ FACT_CHECK : "verified by"
    ARTICLE ||--o{ ARTICLE_EMBEDDING : "has embeddings"

    %% European Parliament Entities
    EP_PLENARY_SESSION ||--o{ EP_AGENDA_ITEM : "contains"
    EP_PLENARY_SESSION ||--o{ EP_VOTE : "includes"
    EP_PLENARY_SESSION ||--o{ EP_DEBATE : "features"
    EP_PLENARY_SESSION ||--o{ ARTICLE : "mentioned in"

    EP_COMMITTEE ||--o{ EP_COMMITTEE_MEETING : "holds"
    EP_COMMITTEE_MEETING ||--o{ EP_COMMITTEE_VOTE : "includes"
    EP_COMMITTEE_MEETING ||--o{ ARTICLE : "mentioned in"

    EP_DOCUMENT ||--o{ EP_DOCUMENT_VERSION : "has versions"
    EP_DOCUMENT ||--o{ ARTICLE : "referenced in"

    EP_MEP ||--o{ EP_VOTE : "casts"
    EP_MEP ||--o{ EP_QUESTION : "asks"
    EP_MEP }o--|| EP_POLITICAL_GROUP : "member of"
    EP_MEP }o--|| EP_NATIONAL_PARTY : "represents"

    %% National Parliament Entities
    NATIONAL_PARLIAMENT ||--o{ NATIONAL_SESSION : "conducts"
    NATIONAL_SESSION ||--o{ NATIONAL_VOTE : "includes"
    NATIONAL_SESSION ||--o{ ARTICLE : "covered in"

    NATIONAL_PARLIAMENT ||--o{ NATIONAL_LEGISLATOR : "has members"
    NATIONAL_LEGISLATOR ||--o{ NATIONAL_VOTE : "participates in"

    %% Cross-Parliament Links
    EP_DOCUMENT ||--o{ NATIONAL_LEGISLATION : "influences"
    NATIONAL_LEGISLATION ||--o{ IMPLEMENTATION_TRACKER : "tracked by"

    %% ML & Analytics Entities
    ML_MODEL ||--o{ QUALITY_SCORE : "generates"
    ML_MODEL ||--o{ FACT_CHECK : "performs"
    ML_MODEL ||--o{ TREND_PREDICTION : "predicts"

    TREND_PREDICTION ||--o{ ARTICLE : "forecasts"

    %% Detailed Attributes
    ARTICLE {
        uuid id PK "UUID primary key"
        string slug UK "URL-friendly identifier"
        string type "prospective, retrospective, breaking, analysis"
        string primary_language "ISO 639-1 code"
        timestamp published_at "Publication timestamp"
        timestamp updated_at "Last modification"
        integer read_time_minutes "Estimated read time"
        jsonb metadata "Flexible metadata"
        boolean is_published "Publication status"
        float quality_score_cached "Latest quality score"
    }

    ARTICLE_VERSION {
        uuid id PK
        uuid article_id FK
        integer version_number "Sequential version"
        string content_html "HTML content"
        string content_markdown "Markdown source"
        timestamp created_at "Version timestamp"
        uuid created_by "User/system ID"
        jsonb changes "Diff from previous version"
    }

    ARTICLE_TRANSLATION {
        uuid id PK
        uuid article_id FK
        string language "ISO 639-1 code"
        string title "Translated title"
        string subtitle "Translated subtitle"
        text content_html "Translated HTML"
        string translation_method "LLM, Neural MT, Human"
        float translation_quality "Quality score 0-1"
        timestamp created_at
    }

    ARTICLE_SOURCE {
        uuid id PK
        uuid article_id FK
        string source_type "ep_plenary, ep_committee, ep_document, national"
        string source_id "External ID"
        string source_url "Canonical URL"
        string parliament "EU, DE-BT, FR-AN, etc."
        jsonb source_data "Cached source data"
        string data_hash "SHA-256 integrity"
    }

    ARTICLE_TYPE {
        string code PK "prospective, retrospective, breaking, analysis"
        jsonb labels "Multilingual labels"
        string icon "Icon identifier"
        integer priority "Display priority"
    }

    GENERATION_JOB {
        uuid id PK
        string job_type "scheduled, event_triggered, manual"
        string status "queued, processing, completed, failed"
        timestamp started_at
        timestamp completed_at
        jsonb parameters "Job configuration"
        jsonb result "Output summary"
        text error_log "Error details"
    }

    QUALITY_SCORE {
        uuid id PK
        uuid article_id FK
        uuid model_id FK
        float overall_score "0.0-1.0"
        float readability_score
        float factual_density_score
        float coherence_score
        float grammar_score
        jsonb detailed_metrics
        timestamp scored_at
    }

    FACT_CHECK {
        uuid id PK
        uuid article_id FK
        string status "verified, disputed, unverified"
        float confidence "0.0-1.0"
        jsonb claims "Array of claim objects"
        jsonb verifications "Array of verification results"
        timestamp checked_at
        uuid model_id FK
    }

    ARTICLE_EMBEDDING {
        uuid id PK
        uuid article_id FK
        string embedding_model "text-embedding-3-large, etc."
        vector embedding "768-dim or 1536-dim vector"
        timestamp created_at
    }

    EP_PLENARY_SESSION {
        uuid id PK
        string session_id UK "EP session identifier"
        date session_date
        string session_type "ordinary, extraordinary"
        jsonb title_multilingual
        string location "Strasbourg, Brussels"
        timestamp start_time
        timestamp end_time
    }

    EP_AGENDA_ITEM {
        uuid id PK
        uuid session_id FK
        integer sequence_number
        string item_type "debate, vote, announcement"
        jsonb title_multilingual
        string rapporteur_mep
        text description
    }

    EP_VOTE {
        uuid id PK
        uuid session_id FK
        string vote_id UK "EP vote identifier"
        timestamp vote_time
        string vote_subject
        string vote_type "simple, qualified, unanimous"
        integer votes_for
        integer votes_against
        integer votes_abstention
        string result "passed, rejected"
        jsonb vote_details "MEP-level voting records"
    }

    EP_COMMITTEE {
        string code PK "LIBE, ECON, ENVI, etc."
        jsonb name_multilingual
        string chair_mep
        jsonb members "Array of MEP IDs"
        string policy_area
    }

    EP_COMMITTEE_MEETING {
        uuid id PK
        string committee_code FK
        date meeting_date
        string meeting_type "ordinary, extraordinary, hearing"
        jsonb agenda
        text minutes
    }

    EP_DOCUMENT {
        uuid id PK
        string document_id UK "EP document identifier"
        string document_type "report, resolution, amendment"
        jsonb title_multilingual
        date publication_date
        string author_mep
        string committee_code FK
    }

    EP_MEP {
        uuid id PK
        string mep_id UK "EP MEP identifier"
        string full_name
        string country "ISO 3166-1 alpha-2"
        string political_group_code FK
        string national_party
        date term_start
        date term_end
        jsonb contact_info
        string photo_url
    }

    EP_POLITICAL_GROUP {
        string code PK "EPP, S&D, Renew, etc."
        jsonb name_multilingual
        string ideology
        integer seat_count
        string leader_mep
    }

    NATIONAL_PARLIAMENT {
        string code PK "DE-BT, FR-AN, IT-CD, etc."
        string country "ISO 3166-1 alpha-2"
        jsonb name_multilingual
        string official_website
        string api_endpoint
        string scraper_type
    }

    NATIONAL_SESSION {
        uuid id PK
        string parliament_code FK
        date session_date
        string session_type
        jsonb title
        integer legislation_count
    }

    NATIONAL_VOTE {
        uuid id PK
        uuid session_id FK
        string vote_id UK
        timestamp vote_time
        string subject
        integer votes_for
        integer votes_against
        string result
    }

    NATIONAL_LEGISLATOR {
        uuid id PK
        string parliament_code FK
        string legislator_id UK
        string full_name
        string party
        string constituency
    }

    NATIONAL_LEGISLATION {
        uuid id PK
        string parliament_code FK
        string legislation_id UK
        string title
        date introduced_date
        string status "proposed, passed, rejected"
        uuid related_ep_document_id FK "Links to EP directive"
    }

    IMPLEMENTATION_TRACKER {
        uuid id PK
        uuid ep_document_id FK "EU directive/regulation"
        string country
        string status "not_started, in_progress, completed"
        date transposition_deadline
        date actual_implementation
        text notes
    }

    ML_MODEL {
        uuid id PK
        string model_name "quality_scorer_v2, fact_checker_v1"
        string model_type "classification, regression, NER"
        string framework "pytorch, tensorflow, sklearn"
        float version "1.0, 2.0, etc."
        timestamp trained_at
        jsonb hyperparameters
        float accuracy "Model accuracy metric"
        string artifact_url "S3/storage location"
    }

    TREND_PREDICTION {
        uuid id PK
        uuid model_id FK
        string prediction_type "topic_forecast, vote_prediction"
        string predicted_topic
        float confidence
        date prediction_date
        date forecast_date
        jsonb supporting_data
    }
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---

🗄️ Database Architecture

Multi-Database Strategy

graph TB
    subgraph "Application Layer"
        API[GraphQL/REST API]
    end

    subgraph "Database Layer"
        Postgres[(PostgreSQL + TimescaleDB<br/>Relational + Time-Series)]
        Mongo[(MongoDB<br/>Document Store)]
        Redis[(Redis Cluster<br/>Cache + Pub/Sub)]
        Elastic[(Elasticsearch<br/>Full-Text Search)]
        Neo4j[(Neo4j<br/>Graph Relationships)]
    end

    subgraph "Data Purposes"
        P1[Structured EP Data<br/>Votes, Sessions, MEPs]
        P2[Articles + Metadata<br/>Flexible Schema]
        P3[Hot Data + Sessions<br/>Real-time State]
        P4[Search Index<br/>Multi-language]
        P5[Entity Relationships<br/>Cross-parliament Links]
    end

    API --> Postgres
    API --> Mongo
    API --> Redis
    API --> Elastic
    API --> Neo4j

    Postgres -.-> P1
    Mongo -.-> P2
    Redis -.-> P3
    Elastic -.-> P4
    Neo4j -.-> P5

    style Postgres fill:#336791,color:#fff
    style Mongo fill:#4DB33D,color:#fff
    style Redis fill:#DC382D,color:#fff
    style Elastic fill:#FEC514,color:#000
    style Neo4j fill:#008CC1,color:#fff
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Database Selection Rationale

Database	Purpose	Rationale	Alternatives Considered
PostgreSQL	Structured EP data, relational queries	ACID compliance, mature, excellent JSON support	MySQL, CockroachDB
TimescaleDB	Time-series voting patterns, trends	PostgreSQL extension, no migration needed	InfluxDB, Prometheus
MongoDB	Article content, flexible metadata	Schema flexibility, rich querying	CouchDB, DynamoDB
Redis	Cache, session state, pub/sub	In-memory speed, versatile data structures	Memcached, Hazelcast
Elasticsearch	Full-text search across 14 languages	Multi-language analyzers, relevance tuning	Solr, Typesense
Neo4j	MEP relationships, cross-parliament links	Native graph, Cypher query language	JanusGraph, ArangoDB

---

📊 PostgreSQL Schema (Relational Data)

Core Tables

-- ============================================
-- ARTICLES TABLE
-- ============================================
CREATE TABLE articles (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    slug VARCHAR(255) UNIQUE NOT NULL,
    type VARCHAR(50) NOT NULL REFERENCES article_types(code),
    primary_language VARCHAR(2) NOT NULL,
    published_at TIMESTAMPTZ,
    updated_at TIMESTAMPTZ DEFAULT NOW(),
    read_time_minutes INTEGER,
    metadata JSONB DEFAULT '{}',
    is_published BOOLEAN DEFAULT FALSE,
    quality_score_cached FLOAT,

    -- Full-text search
    search_vector tsvector GENERATED ALWAYS AS (
        to_tsvector('english', coalesce(slug, ''))
    ) STORED,

    -- Indexes
    CONSTRAINT valid_quality_score CHECK (quality_score_cached BETWEEN 0 AND 1)
);

CREATE INDEX idx_articles_published_at ON articles(published_at DESC);
CREATE INDEX idx_articles_type ON articles(type);
CREATE INDEX idx_articles_language ON articles(primary_language);
CREATE INDEX idx_articles_search ON articles USING GIN(search_vector);

-- ============================================
-- ARTICLE VERSIONS (Temporal Versioning)
-- ============================================
CREATE TABLE article_versions (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    article_id UUID NOT NULL REFERENCES articles(id) ON DELETE CASCADE,
    version_number INTEGER NOT NULL,
    content_html TEXT NOT NULL,
    content_markdown TEXT,
    created_at TIMESTAMPTZ DEFAULT NOW(),
    created_by UUID, -- User or system ID
    changes JSONB, -- Diff from previous version

    UNIQUE(article_id, version_number)
);

CREATE INDEX idx_article_versions_article ON article_versions(article_id, version_number DESC);

-- ============================================
-- ARTICLE TRANSLATIONS
-- ============================================
CREATE TABLE article_translations (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    article_id UUID NOT NULL REFERENCES articles(id) ON DELETE CASCADE,
    language VARCHAR(2) NOT NULL,
    title VARCHAR(500) NOT NULL,
    subtitle VARCHAR(1000),
    content_html TEXT NOT NULL,
    translation_method VARCHAR(50), -- 'LLM', 'Neural MT', 'Human'
    translation_quality FLOAT,
    created_at TIMESTAMPTZ DEFAULT NOW(),

    UNIQUE(article_id, language),
    CONSTRAINT valid_translation_quality CHECK (translation_quality BETWEEN 0 AND 1)
);

CREATE INDEX idx_translations_article_lang ON article_translations(article_id, language);

-- ============================================
-- EUROPEAN PARLIAMENT TABLES
-- ============================================

-- Plenary Sessions
CREATE TABLE ep_plenary_sessions (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    session_id VARCHAR(100) UNIQUE NOT NULL,
    session_date DATE NOT NULL,
    session_type VARCHAR(50), -- 'ordinary', 'extraordinary'
    title_multilingual JSONB NOT NULL,
    location VARCHAR(100),
    start_time TIMESTAMPTZ,
    end_time TIMESTAMPTZ
);

CREATE INDEX idx_ep_sessions_date ON ep_plenary_sessions(session_date DESC);

-- Votes (with TimescaleDB for time-series analysis)
CREATE TABLE ep_votes (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    session_id UUID REFERENCES ep_plenary_sessions(id),
    vote_id VARCHAR(100) UNIQUE NOT NULL,
    vote_time TIMESTAMPTZ NOT NULL,
    vote_subject TEXT NOT NULL,
    vote_type VARCHAR(50),
    votes_for INTEGER NOT NULL,
    votes_against INTEGER NOT NULL,
    votes_abstention INTEGER NOT NULL,
    result VARCHAR(50) NOT NULL,
    vote_details JSONB -- MEP-level voting records
);

-- Convert to TimescaleDB hypertable for efficient time-series queries
SELECT create_hypertable('ep_votes', 'vote_time');

CREATE INDEX idx_ep_votes_time ON ep_votes(vote_time DESC);
CREATE INDEX idx_ep_votes_result ON ep_votes(result);

-- MEPs (Members of European Parliament)
CREATE TABLE ep_meps (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    mep_id VARCHAR(100) UNIQUE NOT NULL,
    full_name VARCHAR(255) NOT NULL,
    country CHAR(2) NOT NULL, -- ISO 3166-1 alpha-2
    political_group_code VARCHAR(50) REFERENCES ep_political_groups(code),
    national_party VARCHAR(255),
    term_start DATE,
    term_end DATE,
    contact_info JSONB,
    photo_url VARCHAR(500)
);

CREATE INDEX idx_ep_meps_country ON ep_meps(country);
CREATE INDEX idx_ep_meps_group ON ep_meps(political_group_code);

-- Committees
CREATE TABLE ep_committees (
    code VARCHAR(10) PRIMARY KEY, -- 'LIBE', 'ECON', etc.
    name_multilingual JSONB NOT NULL,
    chair_mep UUID REFERENCES ep_meps(id),
    members JSONB, -- Array of MEP IDs
    policy_area VARCHAR(255)
);

-- ============================================
-- NATIONAL PARLIAMENTS
-- ============================================

CREATE TABLE national_parliaments (
    code VARCHAR(10) PRIMARY KEY, -- 'DE-BT', 'FR-AN', etc.
    country CHAR(2) NOT NULL,
    name_multilingual JSONB NOT NULL,
    official_website VARCHAR(500),
    api_endpoint VARCHAR(500),
    scraper_type VARCHAR(50) -- 'api', 'scraper', 'manual'
);

CREATE TABLE national_sessions (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    parliament_code VARCHAR(10) REFERENCES national_parliaments(code),
    session_date DATE NOT NULL,
    session_type VARCHAR(50),
    title JSONB,
    legislation_count INTEGER
);

CREATE INDEX idx_national_sessions_parliament ON national_sessions(parliament_code, session_date DESC);

-- ============================================
-- ML & ANALYTICS TABLES
-- ============================================

CREATE TABLE quality_scores (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    article_id UUID NOT NULL REFERENCES articles(id) ON DELETE CASCADE,
    model_id UUID REFERENCES ml_models(id),
    overall_score FLOAT NOT NULL,
    readability_score FLOAT,
    factual_density_score FLOAT,
    coherence_score FLOAT,
    grammar_score FLOAT,
    detailed_metrics JSONB,
    scored_at TIMESTAMPTZ DEFAULT NOW(),

    CONSTRAINT valid_overall_score CHECK (overall_score BETWEEN 0 AND 1)
);

CREATE INDEX idx_quality_scores_article ON quality_scores(article_id, scored_at DESC);

CREATE TABLE fact_checks (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    article_id UUID NOT NULL REFERENCES articles(id) ON DELETE CASCADE,
    status VARCHAR(50) NOT NULL, -- 'verified', 'disputed', 'unverified'
    confidence FLOAT,
    claims JSONB NOT NULL, -- Array of claim objects
    verifications JSONB NOT NULL, -- Array of verification results
    checked_at TIMESTAMPTZ DEFAULT NOW(),
    model_id UUID REFERENCES ml_models(id)
);

CREATE INDEX idx_fact_checks_article ON fact_checks(article_id, checked_at DESC);

-- ============================================
-- VECTOR EMBEDDINGS (for semantic search)
-- ============================================
CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE article_embeddings (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    article_id UUID NOT NULL REFERENCES articles(id) ON DELETE CASCADE,
    embedding_model VARCHAR(100) NOT NULL,
    embedding vector(1536), -- Adjust dimension based on model
    created_at TIMESTAMPTZ DEFAULT NOW(),

    UNIQUE(article_id, embedding_model)
);

-- HNSW index for fast similarity search
CREATE INDEX idx_article_embeddings_vector
ON article_embeddings
USING hnsw (embedding vector_cosine_ops);
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Advanced Queries

Time-Series Analysis: Voting Patterns

-- Voting trend analysis over time
SELECT
    time_bucket('1 week', vote_time) AS week,
    vote_type,
    COUNT(*) AS vote_count,
    AVG(CASE WHEN result = 'passed' THEN 1 ELSE 0 END) AS pass_rate,
    AVG(votes_for::FLOAT / NULLIF(votes_for + votes_against + votes_abstention, 0)) AS avg_support_ratio
FROM ep_votes
WHERE vote_time >= NOW() - INTERVAL '1 year'
GROUP BY week, vote_type
ORDER BY week DESC;
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Semantic Search: Similar Articles

-- Find articles similar to a given article
WITH target_article AS (
    SELECT embedding
    FROM article_embeddings
    WHERE article_id = $1
    AND embedding_model = 'text-embedding-3-large'
)
SELECT
    a.id,
    a.slug,
    t.title,
    1 - (ae.embedding <=> ta.embedding) AS similarity
FROM article_embeddings ae
JOIN articles a ON ae.article_id = a.id
JOIN article_translations t ON a.id = t.article_id AND t.language = 'en'
CROSS JOIN target_article ta
WHERE ae.embedding_model = 'text-embedding-3-large'
AND ae.article_id != $1
ORDER BY ae.embedding <=> ta.embedding
LIMIT 10;
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Cross-Parliament Analysis

-- Compare EU directive implementation across countries
SELECT
    ep_doc.document_id,
    ep_doc.title_multilingual->>'en' AS directive_title,
    it.country,
    it.status,
    it.transposition_deadline,
    it.actual_implementation,
    EXTRACT(DAY FROM it.actual_implementation - it.transposition_deadline) AS days_delta
FROM ep_documents ep_doc
JOIN implementation_trackers it ON ep_doc.id = it.ep_document_id
WHERE ep_doc.document_type = 'directive'
AND it.transposition_deadline >= '2024-01-01'
ORDER BY it.transposition_deadline DESC;
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---

📄 MongoDB Schema (Document Store)

Collections Design

// ============================================
// articles_content collection
// ============================================
{
  _id: ObjectId("..."),
  articleId: UUID("..."), // References PostgreSQL articles.id
  slug: "eu-plenary-approves-ai-act-2026",
  type: "retrospective",
  primaryLanguage: "en",

  // Rich content structure
  content: {
    html: "<article>...</article>",
    markdown: "## EU Plenary Approves AI Act...",
    plainText: "The European Parliament today...",

    // Structured content blocks
    blocks: [
      {
        type: "headline",
        level: 1,
        text: "EU Plenary Approves Landmark AI Act"
      },
      {
        type: "paragraph",
        text: "In a historic vote today..."
      },
      {
        type: "quote",
        speaker: "Ursula von der Leyen",
        text: "This legislation sets a global standard...",
        source: "Plenary Speech"
      },
      {
        type: "data_visualization",
        chartType: "bar",
        data: { votesFor: 450, votesAgainst: 150, abstentions: 50 }
      }
    ]
  },

  // Translations embedded for fast access
  translations: {
    "de": {
      title: "EU-Plenum genehmigt KI-Gesetz",
      subtitle: "Historische Abstimmung...",
      contentHtml: "<article>...</article>"
    },
    "fr": {
      title: "Le Parlement européen approuve la loi sur l'IA",
      subtitle: "Vote historique...",
      contentHtml: "<article>...</article>"
    }
    // ... 12 more languages
  },

  // Rich metadata
  metadata: {
    readTimeMinutes: 5,
    wordCount: 1200,

    // SEO
    seo: {
      metaTitle: "EU Plenary Approves AI Act | EP Monitor",
      metaDescription: "The European Parliament today approved...",
      keywords: ["AI Act", "European Parliament", "regulation"],
      ogImage: "https://cdn.epmonitor.com/images/ai-act.jpg"
    },

    // Analytics
    analytics: {
      viewCount: 15420,
      shareCount: 342,
      avgReadTime: 4.8,
      bounceRate: 0.23
    },

    // Content fingerprint
    fingerprint: {
      hash: "sha256:abc123...",
      generatedBy: "gpt-4-turbo",
      generatedAt: ISODate("2026-06-15T14:30:00Z"),
      prompt: "Generate breaking news article..."
    }
  },

  // Sources with full context
  sources: [
    {
      type: "ep_plenary_session",
      id: "PL-2026-06-15",
      url: "https://ep.europa.eu/plenary/2026-06-15",
      title: "Plenary Session - AI Act Vote",
      dataSnapshot: {
        // Cached EP data at time of generation
        voteResult: { for: 450, against: 150, abstention: 50 },
        votingBreakdown: { /* detailed MEP votes */ }
      },
      weight: 0.9 // Source importance
    },
    {
      type: "ep_committee_report",
      id: "LIBE-2026-0123",
      url: "https://ep.europa.eu/committees/LIBE/2026-0123",
      title: "LIBE Committee Report on AI Act",
      weight: 0.5
    }
  ],

  // Publication workflow
  workflow: {
    status: "published",
    createdAt: ISODate("2026-06-15T14:30:00Z"),
    publishedAt: ISODate("2026-06-15T15:00:00Z"),
    lastModifiedAt: ISODate("2026-06-15T16:45:00Z"),
    modifiedBy: "system",

    // Approval chain (if required)
    approvals: [
      {
        approver: "editor@epmonitor.com",
        approvedAt: ISODate("2026-06-15T14:55:00Z"),
        comments: "Fact-check confirmed, approved for publication"
      }
    ]
  },

  // AI-generated features
  ai: {
    qualityScore: {
      overall: 0.87,
      readability: 0.85,
      factualDensity: 0.90,
      coherence: 0.88,
      grammar: 0.91
    },

    factCheck: {
      status: "verified",
      confidence: 0.92,
      claimsVerified: 8,
      claimsFailed: 0,
      checkedAt: ISODate("2026-06-15T14:35:00Z")
    },

    sentiment: {
      overall: "neutral",
      scores: { positive: 0.3, neutral: 0.6, negative: 0.1 }
    },

    // Automatically extracted entities
    entities: [
      { type: "ORGANIZATION", text: "European Parliament", confidence: 0.99 },
      { type: "LAW", text: "AI Act", confidence: 0.98 },
      { type: "PERSON", text: "Ursula von der Leyen", confidence: 0.97 }
    ]
  },

  // Indexing
  createdAt: ISODate("2026-06-15T14:30:00Z"),
  updatedAt: ISODate("2026-06-15T16:45:00Z")
}

// ============================================
// Indexes for MongoDB
// ============================================
db.articles_content.createIndex({ articleId: 1 }, { unique: true });
db.articles_content.createIndex({ slug: 1 });
db.articles_content.createIndex({ type: 1, "workflow.publishedAt": -1 });
db.articles_content.createIndex({ primaryLanguage: 1 });
db.articles_content.createIndex({ "metadata.analytics.viewCount": -1 });
db.articles_content.createIndex({ "workflow.status": 1, "workflow.publishedAt": -1 });

// Full-text search across all languages
db.articles_content.createIndex({
  "content.plainText": "text",
  "translations.de.contentHtml": "text",
  "translations.fr.contentHtml": "text"
  // ... more languages
}, {
  default_language: "english",
  language_override: "primaryLanguage"
});
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---

🔴 Redis Data Structures (Cache & Real-Time)

Cache Patterns

// ============================================
// HOT ARTICLE CACHE
// ============================================
// Key: article:{slug}:{language}
// Type: String (JSON)
// TTL: 5 minutes
{
  "slug": "eu-plenary-approves-ai-act-2026",
  "title": "EU Plenary Approves Landmark AI Act",
  "contentHtml": "<article>...</article>",
  "publishedAt": "2026-06-15T15:00:00Z",
  "qualityScore": 0.87
}

// ============================================
// REAL-TIME EVENT STREAM
// ============================================
// Key: events:plenary
// Type: Stream
// Retention: 24 hours
XADD events:plenary *
  type "vote_completed"
  sessionId "PL-2026-06-15"
  voteId "V-2026-0123"
  result "passed"
  timestamp "1718463600"

// ============================================
// API RATE LIMITING
// ============================================
// Key: ratelimit:{apiKey}:{window}
// Type: String (counter)
// TTL: 1 minute (sliding window)
INCR ratelimit:sk_live_abc123:2026061515
EXPIRE ratelimit:sk_live_abc123:2026061515 60

// ============================================
// REAL-TIME USER SESSIONS
// ============================================
// Key: session:{sessionId}
// Type: Hash
SET session:xyz123
  userId "user-456"
  language "en"
  subscriptions '["plenary", "LIBE"]'
  connectedAt "1718463600"
EXPIRE session:xyz123 3600

// ============================================
// PUB/SUB FOR WEBSOCKETS
// ============================================
// Channel: article:published:{language}
PUBLISH article:published:en '{"articleId":"...", "slug":"..."}'

// Channel: plenary:event
PUBLISH plenary:event '{"type":"vote_started", "sessionId":"..."}'
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---

🔍 Elasticsearch Schema (Full-Text Search)

Index Mapping

{
  "settings": {
    "number_of_shards": 3,
    "number_of_replicas": 2,
    "analysis": {
      "analyzer": {
        "multilingual_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": [
            "lowercase",
            "asciifolding",
            "stop_multilingual",
            "snowball_multilingual"
          ]
        }
      },
      "filter": {
        "stop_multilingual": {
          "type": "stop",
          "stopwords": ["_english_", "_german_", "_french_", "_spanish_"]
        },
        "snowball_multilingual": {
          "type": "snowball",
          "language": "English"
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "articleId": { "type": "keyword" },
      "slug": { "type": "keyword" },
      "type": { "type": "keyword" },
      "primaryLanguage": { "type": "keyword" },

      "title": {
        "type": "text",
        "analyzer": "multilingual_analyzer",
        "fields": {
          "keyword": { "type": "keyword" },
          "english": { "type": "text", "analyzer": "english" },
          "german": { "type": "text", "analyzer": "german" },
          "french": { "type": "text", "analyzer": "french" }
        }
      },

      "content": {
        "type": "text",
        "analyzer": "multilingual_analyzer",
        "term_vector": "with_positions_offsets"
      },

      "translations": {
        "type": "nested",
        "properties": {
          "language": { "type": "keyword" },
          "title": { "type": "text", "analyzer": "multilingual_analyzer" },
          "content": { "type": "text", "analyzer": "multilingual_analyzer" }
        }
      },

      "sources": {
        "type": "nested",
        "properties": {
          "type": { "type": "keyword" },
          "title": { "type": "text" }
        }
      },

      "publishedAt": { "type": "date" },
      "qualityScore": { "type": "float" },

      "entities": {
        "type": "nested",
        "properties": {
          "type": { "type": "keyword" },
          "text": { "type": "keyword" }
        }
      },

      "keywords": { "type": "keyword" }
    }
  }
}
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Search Queries

// Multi-language search with boosting
{
  "query": {
    "bool": {
      "should": [
        {
          "multi_match": {
            "query": "artificial intelligence regulation",
            "fields": [
              "title.english^3",
              "content.english^2",
              "translations.title^1.5",
              "translations.content"
            ],
            "type": "best_fields",
            "fuzziness": "AUTO"
          }
        },
        {
          "nested": {
            "path": "entities",
            "query": {
              "bool": {
                "should": [
                  { "term": { "entities.text": "AI Act" } }
                ]
              }
            },
            "boost": 2.0
          }
        }
      ],
      "filter": [
        { "term": { "primaryLanguage": "en" } },
        { "range": { "publishedAt": { "gte": "now-30d" } } },
        { "range": { "qualityScore": { "gte": 0.7 } } }
      ]
    }
  },
  "highlight": {
    "fields": {
      "content": {},
      "title": {}
    }
  },
  "sort": [
    { "_score": "desc" },
    { "publishedAt": "desc" }
  ]
}
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---

🕸️ Neo4j Graph Schema (Relationships)

Node Types & Relationships

// ============================================
// CREATE NODE TYPES
// ============================================

// MEPs (Members of European Parliament)
CREATE (m:MEP {
  mepId: 'MEP-12345',
  name: 'Jane Smith',
  country: 'DE',
  politicalGroup: 'EPP',
  nationalParty: 'CDU'
})

// Political Groups
CREATE (g:PoliticalGroup {
  code: 'EPP',
  name: 'European People\'s Party',
  ideology: 'Centre-right'
})

// Topics
CREATE (t:Topic {
  topicId: 'AI_REGULATION',
  name: 'Artificial Intelligence Regulation',
  category: 'Digital Policy'
})

// Legislation
CREATE (l:Legislation {
  documentId: 'EP-2026-AI-ACT',
  title: 'AI Act',
  type: 'Regulation',
  status: 'Adopted'
})

// Committees
CREATE (c:Committee {
  code: 'LIBE',
  name: 'Civil Liberties, Justice and Home Affairs'
})

// National Parliaments
CREATE (np:NationalParliament {
  code: 'DE-BT',
  country: 'DE',
  name: 'Deutscher Bundestag'
})

// ============================================
// CREATE RELATIONSHIPS
// ============================================

// MEP membership
MATCH (m:MEP {mepId: 'MEP-12345'})
MATCH (g:PoliticalGroup {code: 'EPP'})
CREATE (m)-[:MEMBER_OF {since: date('2019-07-02')}]->(g)

// MEP committee membership
MATCH (m:MEP {mepId: 'MEP-12345'})
MATCH (c:Committee {code: 'LIBE'})
CREATE (m)-[:SERVES_ON {role: 'Member', since: date('2020-01-15')}]->(c)

// MEP voting on legislation
MATCH (m:MEP {mepId: 'MEP-12345'})
MATCH (l:Legislation {documentId: 'EP-2026-AI-ACT'})
CREATE (m)-[:VOTED {
  vote: 'FOR',
  date: datetime('2026-06-15T14:00:00Z'),
  confidence: 1.0
}]->(l)

// Legislation related to topic
MATCH (l:Legislation {documentId: 'EP-2026-AI-ACT'})
MATCH (t:Topic {topicId: 'AI_REGULATION'})
CREATE (l)-[:ADDRESSES {weight: 1.0}]->(t)

// Cross-parliament influence
MATCH (ep_leg:Legislation {documentId: 'EP-2026-AI-ACT'})
MATCH (nat_leg:NationalLegislation {legislationId: 'DE-AI-IMPL-2027'})
CREATE (ep_leg)-[:INFLUENCES {
  implementationType: 'Transposition',
  deadline: date('2027-12-31')
}]->(nat_leg)

// ============================================
// ADVANCED GRAPH QUERIES
// ============================================

// Find MEPs who frequently vote together (coalition analysis)
MATCH (m1:MEP)-[v1:VOTED]->(l:Legislation)<-[v2:VOTED]-(m2:MEP)
WHERE m1.mepId < m2.mepId AND v1.vote = v2.vote
WITH m1, m2, COUNT(l) AS votesInCommon
WHERE votesInCommon > 10
RETURN m1.name, m2.name, votesInCommon
ORDER BY votesInCommon DESC
LIMIT 20

// Track legislation influence across parliaments
MATCH path = (ep:Legislation {documentId: 'EP-2026-AI-ACT'})
  -[:INFLUENCES*1..3]->(nat:NationalLegislation)
RETURN path

// Find committees with most influence on a topic
MATCH (c:Committee)<-[:SERVES_ON]-(m:MEP)-[:VOTED]->(l:Legislation)-[:ADDRESSES]->(t:Topic {topicId: 'AI_REGULATION'})
WITH c, COUNT(DISTINCT l) AS legislationCount
RETURN c.code, c.name, legislationCount
ORDER BY legislationCount DESC
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---

🔄 Data Synchronization Strategy

Multi-Database Consistency

sequenceDiagram
    participant API as GraphQL API
    participant Postgres as PostgreSQL
    participant Mongo as MongoDB
    participant Redis as Redis
    participant Elastic as Elasticsearch
    participant Neo4j as Neo4j

    API->>Postgres: 1. Write article metadata
    Postgres-->>API: Article ID

    API->>Mongo: 2. Write article content
    Mongo-->>API: Document ID

    API->>Redis: 3. Cache article
    Redis-->>API: Cached

    API->>Elastic: 4. Index for search
    Elastic-->>API: Indexed

    API->>Neo4j: 5. Create relationships
    Neo4j-->>API: Relationships created

    Note over API,Neo4j: All writes in distributed transaction

    API->>Redis: 6. Invalidate related caches
    API->>Elastic: 7. Trigger reindex if needed
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Eventual Consistency Handling

// src/backend/services/data-sync-coordinator.ts
export class DataSyncCoordinator {
  async publishArticle(article: Article): Promise<void> {
    const transaction = await this.startDistributedTransaction();

    try {
      // 1. PostgreSQL: Structured metadata
      const pgId = await this.postgres.query(
        `
        INSERT INTO articles (slug, type, primary_language, published_at)
        VALUES ($1, $2, $3, $4)
        RETURNING id
      `,
        [article.slug, article.type, article.language, new Date()]
      );

      // 2. MongoDB: Rich content
      await this.mongo.collection('articles_content').insertOne({
        articleId: pgId.rows[0].id,
        content: article.content,
        translations: article.translations,
        metadata: article.metadata,
      });

      // 3. Redis: Hot cache
      await this.redis.setex(
        `article:${article.slug}:${article.language}`,
        300, // 5 minutes
        JSON.stringify(article)
      );

      // 4. Elasticsearch: Search index
      await this.elastic.index({
        index: 'articles',
        id: pgId.rows[0].id,
        body: {
          title: article.title,
          content: article.content,
          language: article.language,
        },
      });

      // 5. Neo4j: Relationships
      await this.neo4j.run(
        `
        MATCH (t:Topic {topicId: $topicId})
        CREATE (a:Article {articleId: $articleId})
        CREATE (a)-[:DISCUSSES]->(t)
      `,
        {
          articleId: pgId.rows[0].id,
          topicId: article.primaryTopic,
        }
      );

      await transaction.commit();

      // 6. Publish event for async consumers
      await this.eventBus.publish('article.published', {
        articleId: pgId.rows[0].id,
        slug: article.slug,
      });
    } catch (error) {
      await transaction.rollback();
      throw new DataSyncError('Failed to publish article', error);
    }
  }

  async handleEventualConsistency() {
    // Periodic reconciliation job
    setInterval(async () => {
      // Find articles in Postgres but not in Elasticsearch
      const missing = await this.findMissingSearchIndices();

      for (const article of missing) {
        await this.reindexArticle(article.id);
      }
    }, 60000); // Every minute
  }
}
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---

📈 Data Migration Plan

Phase-by-Phase Migration

gantt
    title Data Migration Timeline
    dateFormat YYYY-MM-DD

    section Preparation
    Schema design                      :p1, 2026-04-01, 30d
    Migration scripts                  :p2, 2026-04-15, 30d
    Test environment setup             :p3, 2026-05-01, 15d

    section Phase 1
    Migrate EP sessions                :m1, 2026-05-15, 7d
    Migrate EP votes                   :m2, 2026-05-22, 7d
    Migrate MEP data                   :m3, 2026-05-29, 7d

    section Phase 2
    Migrate articles (last 1 year)     :m4, 2026-06-05, 14d
    Build search indices               :m5, 2026-06-12, 7d
    Create graph relationships         :m6, 2026-06-19, 7d

    section Phase 3
    Historical articles (all time)     :m7, 2026-06-26, 21d
    Verification & validation          :m8, 2026-07-17, 7d
    Cutover to new system              :m9, 2026-07-24, 3d
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Migration Scripts

// scripts/migrate-articles.ts
import { createReadStream } from 'fs';
import { parse } from 'node-html-parser';

async function migrateArticles() {
  // Read all HTML files from current static site
  const articles = await findAllArticles('./');

  console.log(`Found ${articles.length} articles to migrate`);

  for (const filePath of articles) {
    const html = await fs.readFile(filePath, 'utf-8');
    const root = parse(html);

    // Extract metadata from HTML
    const metadata = {
      slug: extractSlug(filePath),
      title: root.querySelector('h1')?.text || '',
      publishedAt: extractDateFromFilename(filePath),
      language: extractLanguage(filePath),
      content: root.querySelector('article')?.innerHTML || '',
    };

    // Write to PostgreSQL
    const result = await postgres.query(
      `
      INSERT INTO articles (slug, type, primary_language, published_at)
      VALUES ($1, $2, $3, $4)
      RETURNING id
    `,
      [metadata.slug, 'retrospective', metadata.language, metadata.publishedAt]
    );

    const articleId = result.rows[0].id;

    // Write to MongoDB
    await mongo.collection('articles_content').insertOne({
      articleId,
      content: { html: metadata.content },
      workflow: { status: 'published', publishedAt: metadata.publishedAt },
    });

    // Index in Elasticsearch
    await elastic.index({
      index: 'articles',
      id: articleId,
      body: {
        slug: metadata.slug,
        title: metadata.title,
        content: stripHtml(metadata.content),
        language: metadata.language,
      },
    });

    console.log(`✅ Migrated: ${metadata.slug}`);
  }

  console.log('✅ Migration complete!');
}
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---

📊 Data Quality & Monitoring

Data Quality Checks

-- Daily data quality checks
CREATE OR REPLACE FUNCTION check_data_quality()
RETURNS TABLE(check_name TEXT, status TEXT, details JSONB) AS $$
BEGIN
  -- Check 1: Articles without content in MongoDB
  RETURN QUERY
  SELECT
    'articles_without_content'::TEXT,
    CASE WHEN missing_count > 0 THEN 'FAIL' ELSE 'PASS' END,
    jsonb_build_object('missing_count', missing_count)
  FROM (
    SELECT COUNT(*) AS missing_count
    FROM articles a
    LEFT JOIN mongo_articles_content_view mac ON a.id = mac.article_id
    WHERE mac.article_id IS NULL
    AND a.is_published = TRUE
  ) sub;

  -- Check 2: Quality scores outside valid range
  RETURN QUERY
  SELECT
    'invalid_quality_scores'::TEXT,
    CASE WHEN invalid_count > 0 THEN 'FAIL' ELSE 'PASS' END,
    jsonb_build_object('invalid_count', invalid_count)
  FROM (
    SELECT COUNT(*) AS invalid_count
    FROM quality_scores
    WHERE overall_score NOT BETWEEN 0 AND 1
  ) sub;

  -- Check 3: Orphaned translations
  RETURN QUERY
  SELECT
    'orphaned_translations'::TEXT,
    CASE WHEN orphan_count > 0 THEN 'FAIL' ELSE 'PASS' END,
    jsonb_build_object('orphan_count', orphan_count)
  FROM (
    SELECT COUNT(*) AS orphan_count
    FROM article_translations at
    LEFT JOIN articles a ON at.article_id = a.id
    WHERE a.id IS NULL
  ) sub;
END;
$$ LANGUAGE plpgsql;

-- Schedule daily execution
SELECT cron.schedule('data-quality-check', '0 6 * * *',
  'SELECT * FROM check_data_quality()'
);
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---

🔮 Visionary Data Model Roadmap: 2027-2037

AI-Driven Data Evolution

As AI models evolve — from Anthropic Opus 4.6 (minor updates every ~2.3 months, major annually) through potential AGI — the data model must evolve to support increasingly sophisticated analytical capabilities.

Era	Years	Data Model Paradigm	Key Additions
Near-Term	2027-2029	Multi-DB + Knowledge Graph	Vector embeddings, semantic search, cross-parliament links
Mid-Term	2029-2032	Unified Semantic Data Fabric	Ontology-driven schemas, automated schema evolution, causal data models
Long-Term	2032-2035	Autonomous Data Intelligence	Self-organizing data structures, AI-curated data quality, predictive indexing
Visionary	2035-2037	AGI-Native Data Architecture	Dynamic schema generation, universal parliament ontology, real-time global intelligence

Phase 5: Semantic Data Fabric (2027-2029)

• Universal Parliament Ontology: Formal OWL/RDF ontology mapping legislative concepts across EU and 27 national parliaments — bills, votes, committees, amendments — into a unified semantic model
• AI-Powered Schema Evolution: Anthropic Opus 5.x+ analyzes incoming data patterns and proposes schema migrations automatically, with human approval gates
• Vector Knowledge Base: Store LLM embeddings for all parliamentary documents enabling semantic similarity search across languages and parliaments
• Temporal Graph Database: Full historical graph with time-travel queries supporting "What did Parliament look like on date X?" analysis

Phase 6: Autonomous Data Curation (2029-2032)

• Self-Healing Data Pipelines: AI agents detect data quality issues, identify root causes, and apply corrections with audit trails
• Predictive Data Modeling: ML models anticipate data needs based on upcoming parliamentary calendars and pre-fetch/pre-structure data
• Cross-Lingual Data Unification: Real-time semantic alignment of parliamentary data across 24+ EU languages without manual mapping
• Causal Inference Layer: Data structures supporting "Why did this vote happen?" analysis beyond correlation

Phase 7: Global Democratic Data (2032-2035)

• 50+ Parliament Integration: Pluggable data adapters for parliaments worldwide with automatic schema discovery and mapping
• Real-Time Streaming at Scale: Process millions of parliamentary events per second with sub-100ms query latency globally
• Citizen Impact Data Model: Link legislative data to economic, social, and environmental outcome indicators for impact assessment

Phase 8: AGI-Ready Data (2035-2037)

• Dynamic Schema Generation: AGI systems generate optimal data schemas based on analytical requirements without human intervention
• Universal Legislative Intelligence: Single query interface across all world parliaments in any language with full semantic understanding
• Autonomous Data Governance: AI-driven compliance, classification, and retention management aligned with evolving regulations

---

📚 References

Current State

• Current Data Model
• Current Architecture

Future State

• Future Architecture
• Future Flowchart
• Future State Diagram

External Standards

• ISO 8601 (Date/Time)
• ISO 639-1 (Language Codes)
• ISO 3166-1 (Country Codes)
• JSON Schema Specification
• OpenAPI/GraphQL Schema Standards

---

📝 Change Log

Version	Date	Author	Changes
3.0	2026-02-24	CEO	Added visionary 2027-2037 data model roadmap
2.0	2026-02-20	Data Architect	Updated near-term 2026-2027 roadmap
1.0	2025-02-17	Data Architect	Initial future data model document

---

Document Status: ✅ APPROVED FOR PLANNING
Next Review: 2026-05-24 (Quarterly)
Classification: Public