🚦 Enhancement Request

This is a comprehensive, additive enhancement request for highly advanced historical election cycle analytics covering all requirements from previous discussions:

Use new data enhanced framework-validation sample data from PR #8204.

🇸🇪 Swedish Parliamentary Periods, Election Cycles, & Semesters (Full Context)

• Parliamentary periods in Sweden start each year on the second Tuesday in September (after national elections, if applicable) and end the day before the next period starts.
• National elections are held every 4th year (since 1994) on the second Sunday in September.
• Each election cycle runs from the start of one newly elected parliament to the eve of the next.
• Each parliamentary year is split into two semesters:
  • Autumn semester: mid-September (session opening) to ~25 January
  • Spring semester: ~26 January to next session opening (September)
• For behavioral and trend analysis, the final (spring) semester before a general election is especially significant (campaign impact, policy shifts, attendance spikes/drops, coalition volatility).

Reference: Sveriges riksdag: Parliamentary year structure

🧠 Six Analytical Frameworks — REQUIRE VERIFIED META/META COVERAGE

• Temporal Analysis: 35 supporting views, 20+ risk rules
• Comparative Analysis: 26 supporting views, 15+ risk rules
• Pattern Recognition: 23 supporting views, 12/13 risk rules
• Predictive Intelligence: 14 supporting views, 8/8 risk rules
• Network Analysis: 11 supporting views, 3/4 risk rules
• Decision Intelligence: 5 supporting views, 5/5 risk rules

All trends/statistics MUST address these frameworks at a meta/meta (view-of-views) level, using ONLY existing advanced views as input (see mapping below).

⏩ WHAT MUST BE CREATED: EXPLICIT META/META VIEWS, OUTPUTS, AND MAPPING

Create the following views in election-cycle-views.xml, with window/seasonal partitioning and meta/meta-level composition (absolutely no direct base table use):

Examles below only, use correct columns based in views used in the queries for views.

1. view_election_cycle_temporal_trends

• Purpose: Aggregated longitudinal trends for attendance, bill proposals, voting, and committee activity across election cycles
• Columns:
  • election_cycle_id
  • year
  • semester (autumn/spring)
  • total_sessions
  • avg_attendance_meta
  • bill_proposals_meta
  • voting_events_meta
  • committee_meetings_meta
  • [more as relevant]
• Source mapping: Query and join existing temporal and attendance discipline meta views:
  • e. g., view_decision_temporal_trends, view_politician_behavioral_trends, etc.

2. view_election_cycle_comparative_analysis

• Purpose: Party, committee, and region-level meta-aggregate comparisons per semester and last-semester before election
• Columns:
  • election_cycle_id
  • semester
  • party_id
  • committee_id
  • region_id
  • comparative_metric_1_meta
  • comparative_metric_N_meta
• Source mapping: Aggregate and window over comparative/group risk meta views:
  • e.g., view_party_performance_metrics, view_committee_productivity_matrix, etc.

3. view_election_cycle_anomaly_pattern

• Purpose: High-level anomaly/pattern detection for each semester and election cycle
• Columns:
  • election_cycle_id
  • semester
  • anomaly_type
  • detected_signal_meta
  • contributing_factor_meta
• Source mapping: Built from advanced anomaly and pattern recognition meta views:
  • e. g., view_riksdagen_voting_anomaly_detection, view_politician_behavioral_trends

4. view_election_cycle_predictive_intelligence

• Purpose: Forecasts, risk spikes, and predictive intelligence signals by cycle/semester
• Columns:
  • election_cycle_id
  • semester
  • predictive_risk_meta
  • risk_forecast_meta
  • key_predictive_feature_meta
• Source mapping: From predictive meta views:
  • e.g., view_risk_score_evolution, view_ministry_risk_evolution

5. view_election_cycle_network_analysis

• Purpose: Bloc/coalition structure and network trend mapping per semester/election
• Columns:
  • election_cycle_id
  • semester
  • bloc_id
  • network_centrality_meta
  • bloc_cohesion_score_meta
  • coalition_shift_meta
• Source mapping: Use only meta-level bloc/network views:
  • e.g., view_riksdagen_coalition_alignment_matrix, view_riksdagen_politician_influence_metrics

6. view_election_cycle_decision_intelligence

• Purpose: Policy success, coalition stability, and legislative outcome meta-analysis
• Columns:
  • election_cycle_id
  • policy_id
  • party_id
  • decision_success_meta
  • coalition_stability_meta
  • outcome_segment_meta
• Source mapping: Highest-level decision intelligence meta views:
  • e.g., view_riksdagen_party_decision_flow, view_decision_temporal_trends

📋 Implementation Requirements

• All new views MUST solely query advanced/meta views (no table or primitive views)
• Each output column must be explicitly documented and traceable to one or more input meta views (with SQL comments and/or docs)
• Specify and document the mapping of every column to input view(s) (in SQL and in DATABASE_VIEW_INTELLIGENCE_CATALOG.md)
• Provide usage examples and ensure test suite validation for edge cases (EQ: all 6 frameworks)
• Cross-link all new/updated views, logic, tests, and rationale to framework, PR, and OSINT evidence (DATA_ANALYSIS_INTOP_OSINT.md, PR #8204)

🏁 Value

Delivers auditable, institutional-grade, election-season-aware intelligence. Every new trend is meta/meta (view-of-views), explainable, and framework-mapped. Outputs are composable, extensively tested, and fully grounded in advanced OSINT analytics covering ALL six frameworks with Swedish parliamentary context.

NOTE: This issue is fully additive: nothing from prior requests removed. If a section was present in any prior communication, it remains here. All specifics for view creation, output, mapping, operational context, and documentation are merged. Expand or refine with further requirements as needed.