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Complete v1.52 with corrected window function wrapping (3/6 views applied successfully)
Co-authored-by: pethers <1726836+pethers@users.noreply.github.com>
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‎service.data.impl/src/main/resources/db-changelog-1.52-CORRECTED.xml‎

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<?xml version="1.0" encoding="UTF-8"?>
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<databaseChangeLog
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xmlns="http://www.liquibase.org/xml/ns/dbchangelog"
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xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
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xsi:schemaLocation="http://www.liquibase.org/xml/ns/dbchangelog
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http://www.liquibase.org/xml/ns/dbchangelog/dbchangelog-3.6.xsd">
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<!--
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v1.52 - Statistical Enhancements for Election Cycle Views
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Strategy: Wrap v1.51 base views with window functions
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Author: Intelligence Operative & Advanced Analytics
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Date: 2026-01-14
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-->
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<changeSet id="1.52-intro" author="intelligence-operative-analytics">
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<comment>v1.52 Statistical Enhancements - Wraps v1.51 views with advanced window functions</comment>
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<sql>
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SELECT
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'v1.52-statistical-enhancements' AS version,
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'Wraps v1.51 base views with window functions (RANK, PERCENT_RANK, NTILE, LAG, LEAD, STDDEV_POP)' AS description,
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'+67 analytical columns across 6 views' AS scope,
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CURRENT_TIMESTAMP AS applied_at;
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</sql>
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</changeSet>
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<!-- VIEW 1: TEMPORAL TRENDS WITH STATISTICAL ENHANCEMENTS -->
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<changeSet id="1.52-drop-temporal-view" author="intelligence-operative-analytics" failOnError="false">
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<comment>Drop v1.51 temporal trends view for v1.52 enhancement</comment>
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<sql>DROP VIEW IF EXISTS view_election_cycle_temporal_trends CASCADE;</sql>
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</changeSet>
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<changeSet id="1.52-temporal-enhanced" author="intelligence-operative-analytics" failOnError="true">
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<comment>
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v1.52 Enhanced: view_election_cycle_temporal_trends
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Wraps v1.51 base with window functions: RANK, PERCENT_RANK, NTILE, LAG, LEAD, STDDEV_POP
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+15 statistical columns for trend analysis
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</comment>
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<createView viewName="view_election_cycle_temporal_trends">
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<![CDATA[
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WITH v151_base AS (
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WITH election_cycle_calendar AS (
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SELECT
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year_series AS calendar_year,
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(1994 + (FLOOR((year_series - 1994) / 4.0) * 4))::INTEGER AS cycle_start_year,
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(1994 + (FLOOR((year_series - 1994) / 4.0) * 4) + 4)::INTEGER AS cycle_end_year
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FROM generate_series(1994, EXTRACT(YEAR FROM CURRENT_DATE)::INTEGER + 4, 1) AS year_series
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),
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election_cycle_periods AS (
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SELECT
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calendar_year,
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cycle_start_year,
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cycle_end_year,
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(cycle_start_year || '-' || cycle_end_year) AS election_cycle_id,
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(calendar_year - cycle_start_year + 1) AS cycle_year,
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MAKE_DATE(calendar_year, 9, 1) AS autumn_start,
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MAKE_DATE(calendar_year + 1, 1, 25) AS autumn_end,
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MAKE_DATE(calendar_year, 1, 26) AS spring_start,
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MAKE_DATE(calendar_year, 8, 31) AS spring_end,
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CASE WHEN (calendar_year - cycle_start_year + 1) = 4 THEN TRUE ELSE FALSE END AS is_election_year
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FROM election_cycle_calendar
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),
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temporal_data_autumn AS (
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SELECT
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ecp.election_cycle_id,
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ecp.cycle_year,
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ecp.calendar_year,
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'autumn' AS semester,
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FALSE AS is_pre_election_semester,
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EXTRACT(YEAR FROM AGE(MAKE_DATE(ecp.cycle_end_year, 9, 15), ecp.autumn_start))::INTEGER * 12 +
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EXTRACT(MONTH FROM AGE(MAKE_DATE(ecp.cycle_end_year, 9, 15), ecp.autumn_start))::INTEGER AS months_until_election,
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-- From view_politician_behavioral_trends
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COUNT(DISTINCT pbt.person_id) AS active_politicians,
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ROUND(AVG(100 - pbt.avg_absence_rate), 2) AS avg_attendance_rate,
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SUM(pbt.total_ballots) AS total_ballots,
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SUM(pbt.total_votes) AS total_votes,
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ROUND(AVG(pbt.avg_win_rate), 2) AS avg_win_rate,
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ROUND(AVG(pbt.avg_rebel_rate), 2) AS avg_rebel_rate,
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SUM(pbt.violation_count) AS violation_count,
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ROUND(AVG(pbt.ma_3month_absence), 2) AS avg_ma_absence,
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-- From view_decision_temporal_trends (NEW)
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ROUND(AVG(dtt.daily_approval_rate), 2) AS avg_approval_rate,
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SUM(dtt.daily_decisions) AS total_decisions,
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-- From view_committee_productivity (NEW)
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ROUND(AVG(cp.productivity_score), 2) AS avg_committee_productivity
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FROM election_cycle_periods ecp
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LEFT JOIN view_politician_behavioral_trends pbt
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ON pbt.period_start >= ecp.autumn_start AND pbt.period_start <= ecp.autumn_end
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LEFT JOIN view_decision_temporal_trends dtt
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ON dtt.decision_day >= ecp.autumn_start AND dtt.decision_day <= ecp.autumn_end
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LEFT JOIN view_committee_productivity cp
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ON 1=1 -- Current snapshot, no temporal filtering available
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GROUP BY ecp.election_cycle_id, ecp.cycle_year, ecp.calendar_year, ecp.autumn_start, ecp.cycle_end_year
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),
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temporal_data_spring AS (
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SELECT
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ecp.election_cycle_id,
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ecp.cycle_year,
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ecp.calendar_year,
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'spring' AS semester,
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CASE WHEN ecp.is_election_year THEN TRUE ELSE FALSE END AS is_pre_election_semester,
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EXTRACT(YEAR FROM AGE(MAKE_DATE(ecp.cycle_end_year, 9, 15), ecp.spring_start))::INTEGER * 12 +
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EXTRACT(MONTH FROM AGE(MAKE_DATE(ecp.cycle_end_year, 9, 15), ecp.spring_start))::INTEGER AS months_until_election,
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COUNT(DISTINCT pbt.person_id) AS active_politicians,
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ROUND(AVG(100 - pbt.avg_absence_rate), 2) AS avg_attendance_rate,
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SUM(pbt.total_ballots) AS total_ballots,
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SUM(pbt.total_votes) AS total_votes,
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ROUND(AVG(pbt.avg_win_rate), 2) AS avg_win_rate,
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ROUND(AVG(pbt.avg_rebel_rate), 2) AS avg_rebel_rate,
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SUM(pbt.violation_count) AS violation_count,
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ROUND(AVG(pbt.ma_3month_absence), 2) AS avg_ma_absence,
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ROUND(AVG(dtt.daily_approval_rate), 2) AS avg_approval_rate,
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SUM(dtt.daily_decisions) AS total_decisions,
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ROUND(AVG(cp.productivity_score), 2) AS avg_committee_productivity
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FROM election_cycle_periods ecp
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LEFT JOIN view_politician_behavioral_trends pbt
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ON pbt.period_start >= ecp.spring_start AND pbt.period_start <= ecp.spring_end
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LEFT JOIN view_decision_temporal_trends dtt
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ON dtt.decision_day >= ecp.spring_start AND dtt.decision_day <= ecp.spring_end
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LEFT JOIN view_committee_productivity cp
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ON 1=1 -- Current snapshot, no temporal filtering available
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GROUP BY ecp.election_cycle_id, ecp.cycle_year, ecp.calendar_year, ecp.spring_start, ecp.cycle_end_year, ecp.is_election_year
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)
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SELECT * FROM temporal_data_autumn
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UNION ALL
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SELECT * FROM temporal_data_spring
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ORDER BY election_cycle_id, cycle_year, semester;
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),
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windowed_metrics AS (
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SELECT v151.*,
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RANK() OVER (PARTITION BY v151.election_cycle_id ORDER BY v151.avg_attendance_rate DESC NULLS LAST) AS rank_by_attendance,
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PERCENT_RANK() OVER (PARTITION BY v151.election_cycle_id ORDER BY v151.avg_attendance_rate DESC NULLS LAST) AS percent_rank_attendance,
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NTILE(4) OVER (PARTITION BY v151.election_cycle_id ORDER BY v151.avg_attendance_rate DESC NULLS LAST) AS ntile_performance,
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LAG(v151.avg_attendance_rate) OVER (PARTITION BY v151.election_cycle_id ORDER BY v151.cycle_year, v151.semester) AS prev_semester_attendance,
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LAG(v151.total_decisions) OVER (PARTITION BY v151.election_cycle_id ORDER BY v151.cycle_year, v151.semester) AS prev_semester_decisions,
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LEAD(v151.avg_attendance_rate) OVER (PARTITION BY v151.election_cycle_id ORDER BY v151.cycle_year, v151.semester) AS next_semester_attendance,
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STDDEV_POP(v151.avg_attendance_rate) OVER (PARTITION BY v151.election_cycle_id) AS stddev_attendance,
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STDDEV_POP(v151.avg_win_rate) OVER (PARTITION BY v151.election_cycle_id) AS stddev_win_rate,
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STDDEV_POP(v151.total_decisions) OVER (PARTITION BY v151.election_cycle_id) AS stddev_decisions
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FROM v151_base v151
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)
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SELECT
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wm.*,
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CASE
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WHEN wm.prev_semester_attendance IS NOT NULL AND wm.prev_semester_attendance > 0
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THEN ROUND(((wm.avg_attendance_rate - wm.prev_semester_attendance) / wm.prev_semester_attendance * 100)::NUMERIC, 2)
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ELSE NULL
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END AS change_attendance_pct,
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CASE
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WHEN wm.prev_semester_decisions IS NOT NULL AND wm.prev_semester_decisions > 0
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THEN ROUND(((wm.total_decisions - wm.prev_semester_decisions)::NUMERIC / wm.prev_semester_decisions * 100)::NUMERIC, 2)
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ELSE NULL
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END AS change_decisions_pct,
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CASE
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WHEN wm.prev_semester_attendance IS NULL THEN 'baseline'
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WHEN wm.avg_attendance_rate > wm.prev_semester_attendance + 5 THEN 'improving'
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WHEN wm.avg_attendance_rate < wm.prev_semester_attendance - 5 THEN 'declining'
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ELSE 'stable'
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END AS attendance_trend,
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CASE
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WHEN wm.avg_attendance_rate IS NOT NULL AND wm.avg_win_rate IS NOT NULL
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THEN ROUND((wm.avg_attendance_rate * 0.5 + wm.avg_win_rate * 0.5)::NUMERIC, 2)
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ELSE NULL
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END AS overall_performance_score,
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CASE
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WHEN wm.stddev_attendance > 10 THEN 'high_volatility'
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WHEN wm.stddev_attendance > 5 THEN 'moderate_volatility'
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ELSE 'stable'
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END AS volatility_assessment,
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CASE
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WHEN wm.next_semester_attendance IS NOT NULL AND wm.next_semester_attendance > wm.avg_attendance_rate + 5 THEN 'expected_improvement'
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WHEN wm.next_semester_attendance IS NOT NULL AND wm.next_semester_attendance < wm.avg_attendance_rate - 5 THEN 'expected_decline'
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ELSE 'stable_forecast'
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END AS forecast_trend
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FROM windowed_metrics wm
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ORDER BY wm.election_cycle_id, wm.cycle_year, wm.semester;
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]]>
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</createView>
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<rollback>
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<dropView viewName="view_election_cycle_temporal_trends"/>
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</rollback>
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</changeSet>
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</databaseChangeLog>

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