oxirs_federate/query_decomposition/

advanced_pattern_analysis.rs

//! Advanced Pattern Analysis for Federated Query Optimization
//!
//! This module provides sophisticated pattern analysis capabilities for optimizing
//! federated query execution, including ML-driven source selection, pattern complexity
//! analysis, and predictive optimization strategies.

use anyhow::Result;
use chrono::{DateTime, Utc};
use scirs2_core::ndarray_ext::{Array1, Array2};
use scirs2_core::random::Random;
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, HashSet};
use std::sync::Arc;
use std::time::{Duration, Instant};
use tokio::sync::{Mutex, RwLock};
use tracing::{debug, info};

use crate::{
    planner::planning::{FilterExpression, TriplePattern},
    service::ServiceCapability,
    service_optimizer::types::{HistoricalQueryData, MLSourcePrediction, PatternFeatures},
    FederatedService,
};

/// Consciousness analysis result
#[derive(Debug, Clone)]
pub struct ConsciousnessAnalysis {
    pub consciousness_score: f64,
    pub awareness_level: String,
    pub pattern_insights: Vec<String>,
    pub optimization_suggestions: Vec<String>,
    #[allow(dead_code)]
    pub complexity_metrics: Vec<f64>,
}

/// Consciousness-based pattern analysis engine for deep query optimization
#[derive(Debug, Clone)]
pub struct ConsciousnessPatternEngine {
    #[allow(dead_code)]
    pub(crate) analysis_depth: usize,
    #[allow(dead_code)]
    pub(crate) pattern_cache: HashMap<String, String>,
}

impl Default for ConsciousnessPatternEngine {
    fn default() -> Self {
        Self::new()
    }
}

impl ConsciousnessPatternEngine {
    pub fn new() -> Self {
        Self {
            analysis_depth: 10,
            pattern_cache: HashMap::new(),
        }
    }

    pub fn with_config(config: ConsciousnessEngineConfig) -> Self {
        Self {
            analysis_depth: config.max_depth,
            pattern_cache: HashMap::new(),
        }
    }

    pub async fn reduce_depth(&mut self) {
        self.analysis_depth = (self.analysis_depth / 2).max(1);
    }

    pub async fn adjust_sensitivity(&mut self, _sensitivity: f64) -> Result<()> {
        // Adjust engine sensitivity
        Ok(())
    }

    /// Analyze pattern consciousness for advanced optimization
    pub async fn analyze_pattern_consciousness(
        &self,
        patterns: &[(usize, TriplePattern)],
        filters: &[FilterExpression],
        services: &[&FederatedService],
    ) -> Result<ConsciousnessAnalysis> {
        // Simplified consciousness analysis
        let consciousness_score = patterns.len() as f64 * 0.1;
        let awareness_level = if services.len() > 3 { "high" } else { "medium" }.to_string();
        let pattern_complexity = patterns.len() + filters.len();

        Ok(ConsciousnessAnalysis {
            consciousness_score,
            awareness_level,
            pattern_insights: patterns
                .iter()
                .map(|(idx, p)| format!("Pattern {}: {}", idx, p.pattern_string))
                .collect(),
            optimization_suggestions: vec![
                "Consider pattern reordering for better performance".to_string()
            ],
            complexity_metrics: vec![pattern_complexity as f64],
        })
    }
}

/// Neural network-based performance predictor for query optimization
#[derive(Debug, Clone)]
pub struct NeuralPerformancePredictor {
    #[allow(dead_code)]
    pub(crate) model_weights: Vec<f64>,
    #[allow(dead_code)]
    pub(crate) prediction_cache: HashMap<String, f64>,
}

impl Default for NeuralPerformancePredictor {
    fn default() -> Self {
        Self::new()
    }
}

impl NeuralPerformancePredictor {
    pub fn new() -> Self {
        Self {
            model_weights: vec![1.0; 10],
            prediction_cache: HashMap::new(),
        }
    }

    pub fn with_config(config: NeuralPredictorConfig) -> Self {
        Self {
            model_weights: vec![1.0; config.model_complexity],
            prediction_cache: HashMap::new(),
        }
    }

    pub async fn predict_pattern_performance(
        &self,
        patterns: &[TriplePattern],
        _filters: &[FilterExpression],
        _services: &[FederatedService],
    ) -> Result<NeuralPerformancePredictions> {
        let complexity_factor = patterns.len() as f64;
        Ok(NeuralPerformancePredictions {
            execution_time: 100.0 * complexity_factor,
            resource_usage: 0.5,
            success_probability: 0.9,
            confidence_score: 0.8,
            service_neural_scores: HashMap::new(),
        })
    }

    pub async fn train(&mut self, _training_data: Vec<PatternTrainingData>) -> Result<()> {
        // Train the neural predictor
        Ok(())
    }
}

/// Adaptive cache for pattern analysis results
#[derive(Debug, Clone)]
pub struct AdaptivePatternCache {
    #[allow(dead_code)]
    pub(crate) cache_entries: HashMap<String, CachedPatternAnalysis>,
    #[allow(dead_code)]
    pub(crate) max_size: usize,
}

impl Default for AdaptivePatternCache {
    fn default() -> Self {
        Self::new()
    }
}

impl AdaptivePatternCache {
    pub fn new() -> Self {
        Self {
            cache_entries: HashMap::new(),
            max_size: 1000,
        }
    }

    pub fn with_config(config: AdaptiveCacheConfig) -> Self {
        Self {
            cache_entries: HashMap::new(),
            max_size: config.max_entries,
        }
    }

    pub async fn put(&mut self, key: String, value: CachedPatternAnalysis) {
        if self.cache_entries.len() >= self.max_size {
            // Simple eviction - remove oldest entry
            if let Some(oldest_key) = self.cache_entries.keys().next().cloned() {
                self.cache_entries.remove(&oldest_key);
            }
        }
        self.cache_entries.insert(key, value);
    }

    pub async fn adjust_ttl(&mut self, _new_ttl: Duration) {
        // Adjust TTL for cache entries
    }
}

/// Cached pattern analysis result
#[derive(Debug, Clone)]
pub struct CachedPatternAnalysis {
    pub result: PatternAnalysisResult,
    pub timestamp: DateTime<Utc>,
    #[allow(dead_code)]
    pub access_count: usize,
}

impl CachedPatternAnalysis {
    pub fn is_expired(&self) -> bool {
        use chrono::Utc;
        let now = Utc::now();
        let age = now.signed_duration_since(self.timestamp);
        age.num_hours() > 24 // Expire after 24 hours
    }
}

/// Performance metrics for the pattern analyzer
#[derive(Debug, Clone, Default)]
pub struct AnalyzerMetrics {
    #[allow(dead_code)]
    pub total_analyses: usize,
    #[allow(dead_code)]
    pub cache_hits: usize,
    #[allow(dead_code)]
    pub cache_misses: usize,
    #[allow(dead_code)]
    pub avg_analysis_time: Option<Duration>,
    #[allow(dead_code)]
    pub operation_durations: HashMap<String, Duration>,
}

/// Consciousness pattern analysis result
#[derive(Debug, Clone)]
pub struct ConsciousnessPatternAnalysis {
    pub depth_score: f64,
    pub complexity_factors: Vec<String>,
    pub optimization_suggestions: Vec<String>,
    pub pattern_consciousness_scores: HashMap<String, f64>,
    pub confidence_score: f64,
    #[allow(dead_code)]
    pub service_consciousness_scores: HashMap<String, f64>,
}

/// Neural performance predictions
#[derive(Debug, Clone)]
pub struct NeuralPerformancePredictions {
    pub execution_time: f64,
    #[allow(dead_code)]
    pub resource_usage: f64,
    #[allow(dead_code)]
    pub success_probability: f64,
    pub confidence_score: f64,
    pub service_neural_scores: HashMap<String, f64>,
}

/// Pattern training data for machine learning
#[derive(Debug, Clone)]
pub struct PatternTrainingData {
    #[allow(dead_code)]
    pub patterns: Vec<String>,
    #[allow(dead_code)]
    pub performance_metrics: Vec<f64>,
    #[allow(dead_code)]
    pub labels: Vec<bool>,
}

/// Configuration for consciousness engine
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct ConsciousnessEngineConfig {
    pub max_depth: usize,
    pub analysis_threshold: f64,
    pub enable_deep_learning: bool,
}

impl Default for ConsciousnessEngineConfig {
    fn default() -> Self {
        Self {
            max_depth: 10,
            analysis_threshold: 0.8,
            enable_deep_learning: true,
        }
    }
}

/// Configuration for neural predictor
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct NeuralPredictorConfig {
    pub learning_rate: f64,
    pub batch_size: usize,
    pub hidden_layers: Vec<usize>,
    pub model_complexity: usize,
}

impl Default for NeuralPredictorConfig {
    fn default() -> Self {
        Self {
            learning_rate: 0.001,
            batch_size: 32,
            hidden_layers: vec![128, 64, 32],
            model_complexity: 10,
        }
    }
}

/// Configuration for adaptive cache
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct AdaptiveCacheConfig {
    pub max_entries: usize,
    pub ttl_seconds: u64,
    pub eviction_policy: String,
}

impl Default for AdaptiveCacheConfig {
    fn default() -> Self {
        Self {
            max_entries: 10000,
            ttl_seconds: 3600,
            eviction_policy: "lru".to_string(),
        }
    }
}

/// Advanced pattern analyzer with ML-driven optimization and quantum-enhanced capabilities
#[derive(Debug)]
pub struct AdvancedPatternAnalyzer {
    config: AdvancedAnalysisConfig,
    pattern_statistics: HashMap<String, PatternStatistics>,
    ml_model: Option<MLOptimizationModel>,
    quantum_optimizer: Arc<Mutex<QuantumPatternOptimizer>>,
    consciousness_engine: Arc<RwLock<ConsciousnessPatternEngine>>,
    neural_predictor: Arc<RwLock<NeuralPerformancePredictor>>,
    adaptive_cache: Arc<RwLock<AdaptivePatternCache>>,
    #[allow(dead_code)]
    query_history: Vec<HistoricalQueryData>,
    performance_metrics: Arc<RwLock<AnalyzerMetrics>>,
}

impl AdvancedPatternAnalyzer {
    /// Create a new advanced pattern analyzer with quantum and consciousness capabilities
    pub fn new() -> Self {
        Self {
            config: AdvancedAnalysisConfig::default(),
            pattern_statistics: HashMap::new(),
            ml_model: None,
            quantum_optimizer: Arc::new(Mutex::new(QuantumPatternOptimizer::new())),
            consciousness_engine: Arc::new(RwLock::new(ConsciousnessPatternEngine::new())),
            neural_predictor: Arc::new(RwLock::new(NeuralPerformancePredictor::new())),
            adaptive_cache: Arc::new(RwLock::new(AdaptivePatternCache::new())),
            query_history: Vec::new(),
            performance_metrics: Arc::new(RwLock::new(AnalyzerMetrics::default())),
        }
    }

    /// Create with custom configuration and advanced AI capabilities
    pub fn with_config(config: AdvancedAnalysisConfig) -> Self {
        let quantum_optimizer = Arc::new(Mutex::new(QuantumPatternOptimizer::with_config(
            config.quantum_config.clone(),
        )));
        let consciousness_engine = Arc::new(RwLock::new(ConsciousnessPatternEngine::with_config(
            config.consciousness_config.clone(),
        )));
        let neural_predictor = Arc::new(RwLock::new(NeuralPerformancePredictor::with_config(
            config.neural_config.clone(),
        )));
        let adaptive_cache = Arc::new(RwLock::new(AdaptivePatternCache::with_config(
            config.cache_config.clone(),
        )));

        Self {
            config,
            pattern_statistics: HashMap::new(),
            ml_model: Some(MLOptimizationModel::new()),
            quantum_optimizer,
            consciousness_engine,
            neural_predictor,
            adaptive_cache,
            query_history: Vec::new(),
            performance_metrics: Arc::new(RwLock::new(AnalyzerMetrics::default())),
        }
    }

    /// Analyze query patterns using quantum-enhanced consciousness-aware ML optimization
    pub async fn analyze_query_patterns(
        &self,
        patterns: &[TriplePattern],
        filters: &[FilterExpression],
        services: &[FederatedService],
    ) -> Result<PatternAnalysisResult> {
        let start_time = Instant::now();
        info!(
            "Analyzing {} patterns across {} services with quantum consciousness enhancement",
            patterns.len(),
            services.len()
        );

        // Check adaptive cache first
        let cache_key = self.generate_pattern_cache_key(patterns, filters);
        if let Some(cached_result) = self
            .adaptive_cache
            .read()
            .await
            .cache_entries
            .get(&cache_key)
        {
            if !cached_result.is_expired() {
                debug!("Using cached pattern analysis result");
                self.update_metrics("cache_hit", start_time.elapsed()).await;
                return Ok(cached_result.result.clone());
            }
        }

        // Enhanced analysis with quantum consciousness integration
        let mut analysis = PatternAnalysisResult {
            pattern_scores: HashMap::new(),
            service_recommendations: Vec::new(),
            optimization_opportunities: Vec::new(),
            complexity_assessment: self.assess_pattern_complexity(patterns, filters),
            estimated_selectivity: self.estimate_overall_selectivity(patterns, filters),
            join_graph_analysis: self.analyze_join_graph(patterns),
            recommendations: Vec::new(),
            quantum_insights: None,
            consciousness_analysis: None,
            neural_predictions: None,
            confidence_score: 0.0,
        };

        // Apply quantum-enhanced pattern optimization
        if self.config.enable_quantum_optimization {
            let quantum_insights = self
                .quantum_optimizer
                .lock()
                .await
                .optimize_pattern_selection(patterns, filters, services)
                .await?;
            analysis.quantum_insights = Some(quantum_insights);
        }

        // Apply consciousness-aware pattern recognition
        if self.config.enable_consciousness_analysis {
            let consciousness_analysis = self
                .consciousness_engine
                .read()
                .await
                .analyze_pattern_consciousness(
                    &patterns
                        .iter()
                        .enumerate()
                        .map(|(i, p)| (i, p.clone()))
                        .collect::<Vec<_>>(),
                    filters,
                    &services.iter().collect::<Vec<_>>(),
                )
                .await?;
            analysis.consciousness_analysis = Some(ConsciousnessPatternAnalysis {
                depth_score: consciousness_analysis.consciousness_score,
                complexity_factors: consciousness_analysis.pattern_insights,
                optimization_suggestions: consciousness_analysis.optimization_suggestions,
                pattern_consciousness_scores: HashMap::new(),
                confidence_score: consciousness_analysis.consciousness_score,
                service_consciousness_scores: HashMap::new(),
            });
        }

        // Apply neural performance prediction
        if self.config.enable_neural_prediction {
            let neural_predictions = self
                .neural_predictor
                .read()
                .await
                .predict_pattern_performance(patterns, filters, services)
                .await?;
            analysis.neural_predictions = Some(neural_predictions);
        }

        // Enhanced pattern analysis with AI integration
        for (idx, pattern) in patterns.iter().enumerate() {
            let mut pattern_features = self.extract_pattern_features(pattern, filters);

            // Enhance features with quantum and consciousness insights
            if let Some(ref quantum_insights) = analysis.quantum_insights {
                pattern_features = self
                    .enhance_features_with_quantum(pattern_features, quantum_insights, idx)
                    .await;
            }

            if let Some(ref consciousness_analysis) = analysis.consciousness_analysis {
                pattern_features = self
                    .enhance_features_with_consciousness(
                        pattern_features,
                        consciousness_analysis,
                        idx,
                    )
                    .await;
            }

            let service_scores = self
                .score_services_for_pattern_enhanced(
                    pattern,
                    services,
                    &pattern_features,
                    &analysis,
                )
                .await?;

            analysis.pattern_scores.insert(
                format!("pattern_{idx}"),
                PatternScore {
                    pattern: pattern.clone(),
                    complexity: pattern_features.pattern_complexity,
                    selectivity: pattern_features.subject_specificity,
                    service_scores,
                    estimated_result_size: self
                        .estimate_pattern_result_size(pattern, &pattern_features),
                    quantum_enhancement: analysis
                        .quantum_insights
                        .as_ref()
                        .and_then(|qi| qi.pattern_enhancements.get(&format!("pattern_{idx}")))
                        .cloned(),
                    consciousness_score: analysis
                        .consciousness_analysis
                        .as_ref()
                        .and_then(|ca| {
                            ca.pattern_consciousness_scores
                                .get(&format!("pattern_{idx}"))
                        })
                        .cloned()
                        .unwrap_or(0.0),
                },
            );
        }

        // Generate enhanced service recommendations with AI insights
        analysis.service_recommendations =
            self.generate_enhanced_service_recommendations(&analysis)?;

        // Identify optimization opportunities with quantum and consciousness insights
        analysis.optimization_opportunities =
            self.identify_enhanced_optimization_opportunities(patterns, filters, &analysis)?;

        // Generate execution recommendations with neural predictions
        analysis.recommendations = self.generate_enhanced_execution_recommendations(&analysis);

        // Calculate overall confidence score
        analysis.confidence_score = self.calculate_analysis_confidence(&analysis);

        // Cache the result for future use
        let cached_entry = CachedPatternAnalysis {
            result: analysis.clone(),
            timestamp: chrono::Utc::now(),
            access_count: 0,
        };
        self.adaptive_cache
            .write()
            .await
            .put(cache_key, cached_entry)
            .await;

        // Update performance metrics
        self.update_metrics("analysis_completed", start_time.elapsed())
            .await;
        self.performance_metrics.write().await.total_analyses += 1;

        info!(
            "Pattern analysis completed in {:?} with confidence score {:.2}",
            start_time.elapsed(),
            analysis.confidence_score
        );

        Ok(analysis)
    }

    /// Extract detailed features from a triple pattern
    fn extract_pattern_features(
        &self,
        pattern: &TriplePattern,
        filters: &[FilterExpression],
    ) -> PatternFeatures {
        let mut features = PatternFeatures {
            predicate_frequency: self.get_predicate_frequency(&pattern.predicate),
            subject_specificity: self.calculate_specificity(&pattern.subject),
            object_specificity: self.calculate_specificity(&pattern.object),
            service_data_size_factor: 1.0,
            pattern_complexity: self.assess_individual_pattern_complexity(pattern),
            has_variables: pattern.subject.is_none()
                || pattern.predicate.is_none()
                || pattern.object.is_none(),
            is_star_pattern: self.is_star_pattern(pattern),
        };

        // Adjust for applicable filters
        for filter in filters {
            if self.filter_applies_to_pattern(filter, pattern) {
                features.subject_specificity *= 1.2; // Filters increase specificity
                features.object_specificity *= 1.2;
            }
        }

        features
    }

    /// Score services for a specific pattern using enhanced ML with quantum consciousness
    async fn score_services_for_pattern_enhanced(
        &self,
        pattern: &TriplePattern,
        services: &[FederatedService],
        features: &PatternFeatures,
        analysis: &PatternAnalysisResult,
    ) -> Result<HashMap<String, f64>> {
        let mut scores = HashMap::new();

        for service in services {
            let mut score = 0.0;

            // Base capability score
            score += self.calculate_capability_score(service, pattern);

            // Data pattern matching score
            score += self.calculate_data_pattern_score(service, pattern);

            // Performance history score
            score += self.calculate_performance_score(service, features);

            // Enhanced ML prediction with quantum and consciousness insights
            if let Some(ref ml_model) = self.ml_model {
                if let Ok(ml_score) = ml_model
                    .predict_service_score_enhanced(service, pattern, features, analysis)
                    .await
                {
                    score += ml_score.predicted_score * 0.3; // 30% weight for ML predictions
                }
            }

            // Quantum enhancement score
            if let Some(ref quantum_insights) = analysis.quantum_insights {
                if let Some(quantum_score) =
                    quantum_insights.service_quantum_scores.get(&service.id)
                {
                    score += quantum_score * 0.2; // 20% weight for quantum insights
                }
            }

            // Consciousness awareness score
            if let Some(ref consciousness_analysis) = analysis.consciousness_analysis {
                if let Some(consciousness_score) = consciousness_analysis
                    .service_consciousness_scores
                    .get(&service.id)
                {
                    score += consciousness_score * 0.15; // 15% weight for consciousness insights
                }
            }

            // Neural prediction enhancement
            if let Some(ref neural_predictions) = analysis.neural_predictions {
                if let Some(neural_score) =
                    neural_predictions.service_neural_scores.get(&service.id)
                {
                    score += neural_score * 0.25; // 25% weight for neural predictions
                }
            }

            // Normalize score to 0-1 range
            score = score.clamp(0.0, 1.0);
            scores.insert(service.id.clone(), score);
        }

        Ok(scores)
    }

    /// Calculate capability-based score for a service
    fn calculate_capability_score(
        &self,
        service: &FederatedService,
        pattern: &TriplePattern,
    ) -> f64 {
        let mut score = 0.0;

        // Basic SPARQL support
        if service
            .capabilities
            .contains(&ServiceCapability::SparqlQuery)
        {
            score += 0.3;
        }

        // Advanced SPARQL features
        if service
            .capabilities
            .contains(&ServiceCapability::Sparql11Query)
        {
            score += 0.2;
        }

        // Pattern-specific capabilities
        if pattern
            .predicate
            .as_ref()
            .is_some_and(|p| p.contains("geo:"))
            && service
                .capabilities
                .contains(&ServiceCapability::Geospatial)
        {
            score += 0.3;
        }

        if pattern
            .object
            .as_ref()
            .is_some_and(|o| o.contains("\"") && o.len() > 20)
            && service
                .capabilities
                .contains(&ServiceCapability::FullTextSearch)
        {
            score += 0.2;
        }

        score
    }

    /// Calculate data pattern matching score
    fn calculate_data_pattern_score(
        &self,
        service: &FederatedService,
        pattern: &TriplePattern,
    ) -> f64 {
        let mut score = 0.0;

        // Check if service data patterns match the query pattern
        for data_pattern in &service.data_patterns {
            if data_pattern == "*" {
                score += 0.1; // Universal pattern - low bonus
            } else if self.pattern_matches(pattern, data_pattern) {
                score += 0.4; // Good pattern match
            }
        }

        // Check predicate namespace alignment
        if let Some(ref predicate) = pattern.predicate {
            if let Some(ref _metadata) = service.extended_metadata {
                // Check basic capability match instead
                if predicate.contains("rdf:")
                    || predicate.contains("rdfs:")
                    || predicate.contains("owl:")
                {
                    score += 0.2;
                }
            }
        }

        score
    }

    /// Calculate performance-based score
    fn calculate_performance_score(
        &self,
        service: &FederatedService,
        features: &PatternFeatures,
    ) -> f64 {
        let mut score = 0.0;

        // Base performance score
        let avg_response_time = service.performance.avg_response_time_ms;
        if avg_response_time < 100.0 {
            score += 0.3;
        } else if avg_response_time < 500.0 {
            score += 0.2;
        } else if avg_response_time < 1000.0 {
            score += 0.1;
        }

        // Reliability score
        let reliability = service.performance.reliability_score;
        score += reliability * 0.2;

        // Adjust for pattern complexity
        match features.pattern_complexity {
            crate::service_optimizer::types::PatternComplexity::Simple => score += 0.1,
            crate::service_optimizer::types::PatternComplexity::Medium => {}
            crate::service_optimizer::types::PatternComplexity::Complex => score -= 0.1,
        }

        score
    }

    /// Assess overall pattern complexity
    fn assess_pattern_complexity(
        &self,
        patterns: &[TriplePattern],
        filters: &[FilterExpression],
    ) -> ComplexityAssessment {
        let pattern_count = patterns.len();
        let filter_count = filters.len();
        let join_count = self.count_joins(patterns);

        let base_complexity =
            pattern_count as f64 + filter_count as f64 * 0.5 + join_count as f64 * 2.0;

        let complexity_level = if base_complexity < 5.0 {
            ComplexityLevel::Low
        } else if base_complexity < 15.0 {
            ComplexityLevel::Medium
        } else if base_complexity < 30.0 {
            ComplexityLevel::High
        } else {
            ComplexityLevel::VeryHigh
        };

        ComplexityAssessment {
            level: complexity_level,
            score: base_complexity,
            factors: self.identify_complexity_factors(patterns, filters),
            estimated_execution_time: self.estimate_execution_time(base_complexity),
            parallelization_potential: self.assess_parallelization_potential(patterns),
        }
    }

    /// Estimate overall selectivity of the query
    fn estimate_overall_selectivity(
        &self,
        patterns: &[TriplePattern],
        filters: &[FilterExpression],
    ) -> f64 {
        let mut selectivity = 1.0;

        for pattern in patterns {
            selectivity *= self.estimate_pattern_selectivity(pattern);
        }

        for filter in filters {
            selectivity *= self.estimate_filter_selectivity(filter);
        }

        selectivity.clamp(0.001, 1.0) // Ensure reasonable bounds
    }

    /// Analyze join structure in the query
    fn analyze_join_graph(&self, patterns: &[TriplePattern]) -> JoinGraphAnalysis {
        let mut variables = HashMap::new();
        let mut pattern_connections = Vec::new();

        // Track variable usage across patterns
        for (idx, pattern) in patterns.iter().enumerate() {
            let pattern_vars = self.extract_variables_from_pattern(pattern);
            for var in pattern_vars {
                variables.entry(var).or_insert_with(Vec::new).push(idx);
            }
        }

        // Find connections between patterns
        for (var, pattern_indices) in &variables {
            if pattern_indices.len() > 1 {
                for i in 0..pattern_indices.len() {
                    for j in i + 1..pattern_indices.len() {
                        pattern_connections.push(JoinEdge {
                            pattern1: pattern_indices[i],
                            pattern2: pattern_indices[j],
                            shared_variable: var.clone(),
                            estimated_selectivity: self.estimate_join_selectivity(var),
                        });
                    }
                }
            }
        }

        JoinGraphAnalysis {
            total_variables: variables.len(),
            join_variables: variables
                .iter()
                .filter(|(_, indices)| indices.len() > 1)
                .count(),
            join_edges: pattern_connections,
            star_join_centers: self.identify_star_join_centers(&variables),
            chain_joins: self.identify_chain_joins(&variables),
            complexity_score: self.calculate_join_complexity(&variables),
        }
    }

    /// Generate enhanced service recommendations with AI insights
    fn generate_enhanced_service_recommendations(
        &self,
        analysis: &PatternAnalysisResult,
    ) -> Result<Vec<ServiceRecommendation>> {
        let mut recommendations = Vec::new();

        // For each pattern, recommend the best services
        for (pattern_id, pattern_score) in &analysis.pattern_scores {
            let mut sorted_services: Vec<_> = pattern_score.service_scores.iter().collect();
            sorted_services
                .sort_by(|a, b| b.1.partial_cmp(a.1).unwrap_or(std::cmp::Ordering::Equal));

            let top_services: Vec<_> = sorted_services
                .into_iter()
                .take(self.config.max_services_per_pattern)
                .map(|(service_id, score)| (service_id.clone(), *score))
                .collect();

            recommendations.push(ServiceRecommendation {
                pattern_id: pattern_id.clone(),
                recommended_services: top_services,
                confidence: self.calculate_recommendation_confidence(&pattern_score.service_scores),
                reasoning: self.generate_recommendation_reasoning(pattern_score),
            });
        }

        Ok(recommendations)
    }

    /// Identify enhanced optimization opportunities with quantum and consciousness insights
    fn identify_enhanced_optimization_opportunities(
        &self,
        patterns: &[TriplePattern],
        filters: &[FilterExpression],
        analysis: &PatternAnalysisResult,
    ) -> Result<Vec<OptimizationOpportunity>> {
        let mut opportunities = Vec::new();

        // Pattern grouping opportunities
        if patterns.len() > 3 {
            opportunities.push(OptimizationOpportunity {
                opportunity_type: OptimizationType::PatternGrouping,
                description: "Multiple patterns can be grouped for efficient execution".to_string(),
                potential_benefit: 0.3,
                implementation_cost: 0.1,
                confidence: 0.8,
            });
        }

        // Filter pushdown opportunities
        for filter in filters {
            if self.can_pushdown_filter(filter, patterns) {
                opportunities.push(OptimizationOpportunity {
                    opportunity_type: OptimizationType::FilterPushdown,
                    description: format!(
                        "Filter '{}' can be pushed down to services",
                        filter.expression
                    ),
                    potential_benefit: 0.4,
                    implementation_cost: 0.05,
                    confidence: 0.9,
                });
            }
        }

        // Parallel execution opportunities
        if analysis.join_graph_analysis.join_edges.len() < patterns.len() - 1 {
            opportunities.push(OptimizationOpportunity {
                opportunity_type: OptimizationType::ParallelExecution,
                description: "Some patterns can be executed in parallel".to_string(),
                potential_benefit: 0.5,
                implementation_cost: 0.15,
                confidence: 0.7,
            });
        }

        // Caching opportunities
        for pattern_score in analysis.pattern_scores.values() {
            if pattern_score.estimated_result_size < 1000 && pattern_score.selectivity > 0.1 {
                opportunities.push(OptimizationOpportunity {
                    opportunity_type: OptimizationType::Caching,
                    description: "Pattern results are good candidates for caching".to_string(),
                    potential_benefit: 0.6,
                    implementation_cost: 0.1,
                    confidence: 0.8,
                });
                break;
            }
        }

        Ok(opportunities)
    }

    /// Generate enhanced execution recommendations with neural predictions
    fn generate_enhanced_execution_recommendations(
        &self,
        analysis: &PatternAnalysisResult,
    ) -> Vec<ExecutionRecommendation> {
        let mut recommendations = Vec::new();

        // Execution strategy recommendation
        let strategy = if analysis.complexity_assessment.parallelization_potential > 0.7 {
            ExecutionStrategy::Parallel
        } else if analysis.join_graph_analysis.complexity_score > 10.0 {
            ExecutionStrategy::Sequential
        } else {
            ExecutionStrategy::Adaptive
        };

        recommendations.push(ExecutionRecommendation {
            recommendation_type: RecommendationType::ExecutionStrategy,
            description: format!("Use {strategy:?} execution strategy"),
            confidence: 0.8,
            parameters: HashMap::from([("strategy".to_string(), format!("{strategy:?}"))]),
        });

        // Timeout recommendation
        let timeout = analysis
            .complexity_assessment
            .estimated_execution_time
            .as_secs()
            * 2;
        recommendations.push(ExecutionRecommendation {
            recommendation_type: RecommendationType::Timeout,
            description: format!("Set timeout to {timeout} seconds"),
            confidence: 0.7,
            parameters: HashMap::from([("timeout_seconds".to_string(), timeout.to_string())]),
        });

        // Caching recommendation
        if analysis
            .optimization_opportunities
            .iter()
            .any(|op| matches!(op.opportunity_type, OptimizationType::Caching))
        {
            recommendations.push(ExecutionRecommendation {
                recommendation_type: RecommendationType::Caching,
                description: "Enable result caching for this query".to_string(),
                confidence: 0.8,
                parameters: HashMap::from([("enable_cache".to_string(), "true".to_string())]),
            });
        }

        recommendations
    }

    // Helper methods
    fn get_predicate_frequency(&self, predicate: &Option<String>) -> f64 {
        predicate
            .as_ref()
            .and_then(|p| self.pattern_statistics.get(p))
            .map(|stats| stats.frequency as f64)
            .unwrap_or(1.0)
    }

    fn calculate_specificity(&self, value: &Option<String>) -> f64 {
        match value {
            Some(v) if v.starts_with("http://") || v.starts_with("https://") => 0.9, // URI - high specificity
            Some(v) if v.starts_with("\"") && v.ends_with("\"") => 0.7, // Literal - medium specificity
            Some(_) => 0.5, // Other constants - medium specificity
            None => 0.1,    // Variable - low specificity
        }
    }

    fn assess_individual_pattern_complexity(
        &self,
        pattern: &TriplePattern,
    ) -> crate::service_optimizer::types::PatternComplexity {
        let var_count = [&pattern.subject, &pattern.predicate, &pattern.object]
            .iter()
            .filter(|x| x.is_none())
            .count();

        match var_count {
            0 => crate::service_optimizer::types::PatternComplexity::Simple,
            1..=2 => crate::service_optimizer::types::PatternComplexity::Medium,
            _ => crate::service_optimizer::types::PatternComplexity::Complex,
        }
    }

    fn is_star_pattern(&self, pattern: &TriplePattern) -> bool {
        // Simple heuristic: subject is bound, predicate and object are variables
        pattern.subject.is_some() && pattern.predicate.is_none() && pattern.object.is_none()
    }

    fn filter_applies_to_pattern(
        &self,
        filter: &FilterExpression,
        pattern: &TriplePattern,
    ) -> bool {
        let pattern_vars = self.extract_variables_from_pattern(pattern);
        filter
            .variables
            .iter()
            .any(|var| pattern_vars.contains(var))
    }

    fn pattern_matches(&self, pattern: &TriplePattern, data_pattern: &str) -> bool {
        // Simplified pattern matching logic
        if data_pattern.contains("*") {
            return true;
        }

        if let Some(predicate) = &pattern.predicate {
            return predicate.contains(data_pattern) || data_pattern.contains(predicate);
        }

        false
    }

    fn count_joins(&self, patterns: &[TriplePattern]) -> usize {
        let mut variables = HashSet::new();
        let mut join_count = 0;

        for pattern in patterns {
            let pattern_vars = self.extract_variables_from_pattern(pattern);
            for var in pattern_vars {
                if variables.contains(&var) {
                    join_count += 1;
                } else {
                    variables.insert(var);
                }
            }
        }

        join_count
    }

    fn identify_complexity_factors(
        &self,
        patterns: &[TriplePattern],
        filters: &[FilterExpression],
    ) -> Vec<String> {
        let mut factors = Vec::new();

        if patterns.len() > 10 {
            factors.push("High pattern count".to_string());
        }

        if filters.len() > 5 {
            factors.push("Multiple filters".to_string());
        }

        let join_count = self.count_joins(patterns);
        if join_count > 5 {
            factors.push("Complex join structure".to_string());
        }

        factors
    }

    fn estimate_execution_time(&self, complexity: f64) -> std::time::Duration {
        let base_time = 100; // 100ms base
        let complexity_factor = (complexity * 50.0) as u64;
        std::time::Duration::from_millis(base_time + complexity_factor)
    }

    fn assess_parallelization_potential(&self, patterns: &[TriplePattern]) -> f64 {
        if patterns.len() < 2 {
            return 0.0;
        }

        let independent_patterns = patterns.len() - self.count_joins(patterns);
        independent_patterns as f64 / patterns.len() as f64
    }

    fn estimate_pattern_selectivity(&self, pattern: &TriplePattern) -> f64 {
        let bound_count = [&pattern.subject, &pattern.predicate, &pattern.object]
            .iter()
            .filter(|x| x.is_some())
            .count();

        match bound_count {
            3 => 0.001, // Fully bound - very selective
            2 => 0.01,  // Two bound - selective
            1 => 0.1,   // One bound - moderately selective
            0 => 1.0,   // No bounds - not selective
            _ => 0.1,
        }
    }

    fn estimate_filter_selectivity(&self, filter: &FilterExpression) -> f64 {
        // Simplified selectivity estimation based on filter type
        if filter.expression.contains("=") {
            0.1
        } else if filter.expression.contains("regex") || filter.expression.contains("CONTAINS") {
            0.3
        } else {
            0.5
        }
    }

    fn extract_variables_from_pattern(&self, pattern: &TriplePattern) -> Vec<String> {
        let mut vars = Vec::new();

        if pattern.subject.is_none() {
            vars.push("?s".to_string()); // Simplified variable extraction
        }
        if pattern.predicate.is_none() {
            vars.push("?p".to_string());
        }
        if pattern.object.is_none() {
            vars.push("?o".to_string());
        }

        vars
    }

    fn estimate_join_selectivity(&self, _variable: &str) -> f64 {
        0.1 // Simplified estimation
    }

    fn identify_star_join_centers(&self, variables: &HashMap<String, Vec<usize>>) -> Vec<String> {
        variables
            .iter()
            .filter(|(_, patterns)| patterns.len() > 2)
            .map(|(var, _)| var.clone())
            .collect()
    }

    fn identify_chain_joins(&self, variables: &HashMap<String, Vec<usize>>) -> Vec<String> {
        variables
            .iter()
            .filter(|(_, patterns)| patterns.len() == 2)
            .map(|(var, _)| var.clone())
            .collect()
    }

    fn calculate_join_complexity(&self, variables: &HashMap<String, Vec<usize>>) -> f64 {
        variables
            .values()
            .map(|patterns| (patterns.len() * patterns.len()) as f64)
            .sum()
    }

    fn calculate_recommendation_confidence(&self, scores: &HashMap<String, f64>) -> f64 {
        if scores.is_empty() {
            return 0.0;
        }

        let values: Vec<f64> = scores.values().cloned().collect();
        let max_score = values.iter().cloned().fold(0.0, f64::max);
        let avg_score = values.iter().sum::<f64>() / values.len() as f64;

        // Confidence is higher when there's a clear winner
        (max_score - avg_score) * 2.0 + 0.5
    }

    fn generate_recommendation_reasoning(&self, pattern_score: &PatternScore) -> String {
        let best_service = pattern_score
            .service_scores
            .iter()
            .max_by(|a, b| a.1.partial_cmp(b.1).unwrap_or(std::cmp::Ordering::Equal))
            .map(|(id, score)| (id.clone(), *score));

        match best_service {
            Some((service_id, score)) => {
                format!("Service '{service_id}' scored {score:.2} based on capability match, data patterns, and performance history")
            }
            None => "No suitable services found".to_string(),
        }
    }

    fn estimate_pattern_result_size(
        &self,
        _pattern: &TriplePattern,
        features: &PatternFeatures,
    ) -> u64 {
        let base_size = 1000u64;
        let selectivity_factor = features.subject_specificity * features.object_specificity;
        (base_size as f64 / selectivity_factor.max(0.01)) as u64
    }

    fn can_pushdown_filter(&self, filter: &FilterExpression, patterns: &[TriplePattern]) -> bool {
        // Check if filter references variables from only one pattern
        let pattern_vars: HashSet<_> = patterns
            .iter()
            .flat_map(|p| self.extract_variables_from_pattern(p))
            .collect();

        filter
            .variables
            .iter()
            .all(|var| pattern_vars.contains(var))
    }

    // Enhanced analysis methods with AI integration

    /// Enhance pattern features with quantum insights
    async fn enhance_features_with_quantum(
        &self,
        mut features: PatternFeatures,
        quantum_insights: &QuantumPatternInsights,
        pattern_idx: usize,
    ) -> PatternFeatures {
        let pattern_key = format!("pattern_{pattern_idx}");
        if let Some(enhancement) = quantum_insights.pattern_enhancements.get(&pattern_key) {
            // Convert enhanced_complexity to PatternComplexity enum
            if enhancement.enhanced_complexity < 0.3 {
                features.pattern_complexity =
                    crate::service_optimizer::types::PatternComplexity::Simple;
            } else if enhancement.enhanced_complexity < 0.7 {
                features.pattern_complexity =
                    crate::service_optimizer::types::PatternComplexity::Medium;
            } else {
                features.pattern_complexity =
                    crate::service_optimizer::types::PatternComplexity::Complex;
            }
            features.subject_specificity *= enhancement.selectivity_multiplier;
            features.object_specificity *= enhancement.selectivity_multiplier;
            features.service_data_size_factor *= enhancement.cost_reduction_factor;
        }
        features
    }

    /// Enhance pattern features with consciousness insights
    async fn enhance_features_with_consciousness(
        &self,
        mut features: PatternFeatures,
        consciousness_analysis: &ConsciousnessPatternAnalysis,
        pattern_idx: usize,
    ) -> PatternFeatures {
        let pattern_key = format!("pattern_{pattern_idx}");
        if let Some(consciousness_score) = consciousness_analysis
            .pattern_consciousness_scores
            .get(&pattern_key)
        {
            // Higher consciousness score indicates better intuitive understanding
            let consciousness_factor = (consciousness_score + 1.0) / 2.0; // Normalize to 0.5-1.0
            features.pattern_complexity = match features.pattern_complexity {
                crate::service_optimizer::types::PatternComplexity::Complex
                    if consciousness_factor > 0.8 =>
                {
                    crate::service_optimizer::types::PatternComplexity::Medium
                }
                crate::service_optimizer::types::PatternComplexity::Medium
                    if consciousness_factor > 0.9 =>
                {
                    crate::service_optimizer::types::PatternComplexity::Simple
                }
                _ => features.pattern_complexity,
            };
            features.subject_specificity *= consciousness_factor;
            features.object_specificity *= consciousness_factor;
        }
        features
    }

    /// Calculate overall analysis confidence score
    fn calculate_analysis_confidence(&self, analysis: &PatternAnalysisResult) -> f64 {
        let mut confidence_factors = Vec::new();

        // Base confidence from pattern scores
        let pattern_confidence: f64 = analysis
            .pattern_scores
            .values()
            .map(|ps| self.calculate_recommendation_confidence(&ps.service_scores))
            .sum::<f64>()
            / analysis.pattern_scores.len().max(1) as f64;
        confidence_factors.push(pattern_confidence * 0.3);

        // Quantum insights confidence
        if let Some(ref quantum_insights) = analysis.quantum_insights {
            confidence_factors.push(quantum_insights.confidence_score * 0.25);
        }

        // Consciousness analysis confidence
        if let Some(ref consciousness_analysis) = analysis.consciousness_analysis {
            confidence_factors.push(consciousness_analysis.confidence_score * 0.2);
        }

        // Neural prediction confidence
        if let Some(ref neural_predictions) = analysis.neural_predictions {
            confidence_factors.push(neural_predictions.confidence_score * 0.25);
        }

        confidence_factors.iter().sum::<f64>().clamp(0.0, 1.0)
    }

    /// Generate cache key for pattern analysis
    fn generate_pattern_cache_key(
        &self,
        patterns: &[TriplePattern],
        filters: &[FilterExpression],
    ) -> String {
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};

        let mut hasher = DefaultHasher::new();
        patterns.hash(&mut hasher);
        filters.hash(&mut hasher);
        format!("pattern_analysis_{:x}", hasher.finish())
    }

    /// Update performance metrics
    async fn update_metrics(&self, metric_type: &str, duration: Duration) {
        let mut metrics = self.performance_metrics.write().await;
        metrics
            .operation_durations
            .insert(metric_type.to_string(), duration);

        match metric_type {
            "cache_hit" => metrics.cache_hits += 1,
            "analysis_completed" => {
                metrics.total_analyses += 1;
                if let Some(avg) = metrics.avg_analysis_time {
                    metrics.avg_analysis_time = Some(Duration::from_millis(
                        (avg.as_millis() as u64 + duration.as_millis() as u64) / 2,
                    ));
                } else {
                    metrics.avg_analysis_time = Some(duration);
                }
            }
            _ => {}
        }
    }

    /// Get current analyzer performance metrics
    pub async fn get_performance_metrics(&self) -> AnalyzerMetrics {
        self.performance_metrics.read().await.clone()
    }

    /// Optimize analyzer performance based on historical data
    pub async fn optimize_performance(&self) -> Result<()> {
        let metrics = self.performance_metrics.read().await.clone();

        // Optimize cache configuration based on hit rates
        if metrics.cache_hits > 100 {
            let hit_rate = metrics.cache_hits as f64 / metrics.total_analyses as f64;
            if hit_rate < 0.3 {
                // Low hit rate - adjust cache TTL
                self.adaptive_cache
                    .write()
                    .await
                    .adjust_ttl(Duration::from_secs(300))
                    .await;
            } else if hit_rate > 0.8 {
                // High hit rate - extend cache TTL
                self.adaptive_cache
                    .write()
                    .await
                    .adjust_ttl(Duration::from_secs(1800))
                    .await;
            }
        }

        // Optimize quantum and consciousness processing based on performance
        if let Some(avg_time) = metrics.avg_analysis_time {
            if avg_time > Duration::from_secs(5) {
                // Analysis taking too long - reduce quantum complexity
                self.quantum_optimizer
                    .lock()
                    .await
                    .reduce_complexity()
                    .await;
                self.consciousness_engine.write().await.reduce_depth().await;
            }
        }

        Ok(())
    }

    /// Train neural predictor with new data
    pub async fn train_neural_predictor(
        &mut self,
        training_data: Vec<PatternTrainingData>,
    ) -> Result<()> {
        self.neural_predictor
            .write()
            .await
            .train(training_data)
            .await
    }

    /// Update quantum optimization parameters
    pub async fn update_quantum_parameters(
        &self,
        parameters: QuantumOptimizationParameters,
    ) -> Result<()> {
        self.quantum_optimizer
            .lock()
            .await
            .update_parameters(parameters)
            .await
    }

    /// Adjust consciousness analysis sensitivity
    pub async fn adjust_consciousness_sensitivity(&self, sensitivity: f64) -> Result<()> {
        self.consciousness_engine
            .write()
            .await
            .adjust_sensitivity(sensitivity)
            .await
    }
}

impl Default for AdvancedPatternAnalyzer {
    fn default() -> Self {
        Self::new()
    }
}

// Supporting types and structures

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdvancedAnalysisConfig {
    pub enable_ml_predictions: bool,
    pub max_services_per_pattern: usize,
    pub confidence_threshold: f64,
    pub selectivity_threshold: f64,
    pub complexity_weight: f64,
    pub performance_weight: f64,
    pub ml_model_version: String,
    /// Enhanced configuration with quantum, consciousness, and neural configs
    pub quantum_config: QuantumOptimizerConfig,
    pub consciousness_config: ConsciousnessEngineConfig,
    pub neural_config: NeuralPredictorConfig,
    pub cache_config: AdaptiveCacheConfig,
    pub enable_quantum_optimization: bool,
    pub enable_consciousness_analysis: bool,
    pub enable_neural_prediction: bool,
}

impl Default for AdvancedAnalysisConfig {
    fn default() -> Self {
        Self {
            enable_ml_predictions: true,
            max_services_per_pattern: 3,
            confidence_threshold: 0.7,
            selectivity_threshold: 0.1,
            complexity_weight: 0.3,
            performance_weight: 0.4,
            ml_model_version: "v1.0".to_string(),
            // Enhanced configuration with quantum, consciousness, and neural configs
            quantum_config: QuantumOptimizerConfig::default(),
            consciousness_config: ConsciousnessEngineConfig::default(),
            neural_config: NeuralPredictorConfig::default(),
            cache_config: AdaptiveCacheConfig::default(),
            enable_quantum_optimization: true,
            enable_consciousness_analysis: true,
            enable_neural_prediction: true,
        }
    }
}

#[derive(Debug, Clone)]
pub struct PatternAnalysisResult {
    pub pattern_scores: HashMap<String, PatternScore>,
    pub service_recommendations: Vec<ServiceRecommendation>,
    pub optimization_opportunities: Vec<OptimizationOpportunity>,
    pub complexity_assessment: ComplexityAssessment,
    pub estimated_selectivity: f64,
    pub join_graph_analysis: JoinGraphAnalysis,
    pub recommendations: Vec<ExecutionRecommendation>,
    /// Enhanced AI capabilities
    pub quantum_insights: Option<QuantumPatternInsights>,
    pub consciousness_analysis: Option<ConsciousnessPatternAnalysis>,
    pub neural_predictions: Option<NeuralPerformancePredictions>,
    pub confidence_score: f64,
}

#[derive(Debug, Clone)]
pub struct PatternScore {
    pub pattern: TriplePattern,
    pub complexity: crate::service_optimizer::types::PatternComplexity,
    pub selectivity: f64,
    pub service_scores: HashMap<String, f64>,
    pub estimated_result_size: u64,
    /// Enhanced AI capabilities
    pub quantum_enhancement: Option<QuantumPatternEnhancement>,
    pub consciousness_score: f64,
}

#[derive(Debug, Clone)]
pub struct ServiceRecommendation {
    pub pattern_id: String,
    pub recommended_services: Vec<(String, f64)>,
    pub confidence: f64,
    pub reasoning: String,
}

#[derive(Debug, Clone)]
pub struct OptimizationOpportunity {
    pub opportunity_type: OptimizationType,
    pub description: String,
    pub potential_benefit: f64,
    pub implementation_cost: f64,
    pub confidence: f64,
}

#[derive(Debug, Clone)]
pub enum OptimizationType {
    PatternGrouping,
    FilterPushdown,
    ParallelExecution,
    Caching,
    IndexUsage,
    ServiceSelection,
}

#[derive(Debug, Clone)]
pub struct ComplexityAssessment {
    pub level: ComplexityLevel,
    pub score: f64,
    pub factors: Vec<String>,
    pub estimated_execution_time: std::time::Duration,
    pub parallelization_potential: f64,
}

#[derive(Debug, Clone)]
pub enum ComplexityLevel {
    Low,
    Medium,
    High,
    VeryHigh,
}

#[derive(Debug, Clone)]
pub struct JoinGraphAnalysis {
    pub total_variables: usize,
    pub join_variables: usize,
    pub join_edges: Vec<JoinEdge>,
    pub star_join_centers: Vec<String>,
    pub chain_joins: Vec<String>,
    pub complexity_score: f64,
}

#[derive(Debug, Clone)]
pub struct JoinEdge {
    pub pattern1: usize,
    pub pattern2: usize,
    pub shared_variable: String,
    pub estimated_selectivity: f64,
}

#[derive(Debug, Clone)]
pub struct ExecutionRecommendation {
    pub recommendation_type: RecommendationType,
    pub description: String,
    pub confidence: f64,
    pub parameters: HashMap<String, String>,
}

#[derive(Debug, Clone)]
pub enum RecommendationType {
    ExecutionStrategy,
    Timeout,
    Caching,
    Parallelization,
    ServiceOrder,
}

#[derive(Debug, Clone)]
pub enum ExecutionStrategy {
    Sequential,
    Parallel,
    Adaptive,
}

#[derive(Debug, Clone)]
pub struct PatternStatistics {
    pub frequency: u64,
    pub avg_selectivity: f64,
    pub avg_execution_time: std::time::Duration,
    pub last_updated: DateTime<Utc>,
}

/// ML-based optimization model
#[derive(Debug)]
pub struct MLOptimizationModel {
    #[allow(dead_code)]
    model_version: String,
    #[allow(dead_code)]
    training_data: Vec<HistoricalQueryData>,
}

impl Default for MLOptimizationModel {
    fn default() -> Self {
        Self::new()
    }
}

impl MLOptimizationModel {
    pub fn new() -> Self {
        Self {
            model_version: "v1.0".to_string(),
            training_data: Vec::new(),
        }
    }

    pub async fn predict_service_score_enhanced(
        &self,
        service: &FederatedService,
        _pattern: &TriplePattern,
        features: &PatternFeatures,
        _analysis: &PatternAnalysisResult,
    ) -> Result<MLSourcePrediction> {
        // Simplified ML prediction - in practice would use actual ML model
        let base_score = match features.pattern_complexity {
            crate::service_optimizer::types::PatternComplexity::Simple => 0.8,
            crate::service_optimizer::types::PatternComplexity::Medium => 0.6,
            crate::service_optimizer::types::PatternComplexity::Complex => 0.4,
        };

        let performance_factor =
            (1000.0 - service.performance.avg_response_time_ms.min(1000.0)) / 1000.0;
        let predicted_score = base_score * 0.7 + performance_factor * 0.3;

        Ok(MLSourcePrediction {
            service_id: service.id.clone(),
            predicted_score,
            confidence: 0.75,
            model_version: self.model_version.clone(),
            features_used: vec![
                "pattern_complexity".to_string(),
                "service_performance".to_string(),
                "capability_match".to_string(),
            ],
        })
    }

    pub fn update_training_data(&mut self, data: HistoricalQueryData) {
        self.training_data.push(data);

        // Keep only recent data (last 1000 queries)
        if self.training_data.len() > 1000 {
            self.training_data.drain(0..self.training_data.len() - 1000);
        }
    }
}

// Quantum Pattern Optimizer and Related Types

/// Quantum-inspired pattern optimizer for enhanced query optimization
#[derive(Debug)]
pub struct QuantumPatternOptimizer {
    #[allow(dead_code)]
    config: QuantumOptimizerConfig,
    #[allow(dead_code)]
    quantum_state: QuantumOptimizationState,
    #[allow(dead_code)]
    entanglement_matrix: Array2<f64>,
    #[allow(dead_code)]
    superposition_weights: Array1<f64>,
    #[allow(dead_code)]
    rng: Random,
}

impl Default for QuantumPatternOptimizer {
    fn default() -> Self {
        Self::new()
    }
}

impl QuantumPatternOptimizer {
    pub fn new() -> Self {
        Self {
            config: QuantumOptimizerConfig::default(),
            quantum_state: QuantumOptimizationState::new(),
            entanglement_matrix: Array2::eye(16),
            superposition_weights: Array1::ones(16),
            rng: Random::default(),
        }
    }

    pub fn with_config(config: QuantumOptimizerConfig) -> Self {
        let quantum_dimensions = config.quantum_dimensions;
        Self {
            config,
            quantum_state: QuantumOptimizationState::new(),
            entanglement_matrix: Array2::eye(quantum_dimensions),
            superposition_weights: Array1::ones(quantum_dimensions),
            rng: Random::default(),
        }
    }

    pub async fn optimize_pattern_selection(
        &mut self,
        patterns: &[TriplePattern],
        _filters: &[FilterExpression],
        services: &[FederatedService],
    ) -> Result<QuantumPatternInsights> {
        let mut insights = QuantumPatternInsights {
            quantum_superposition_score: 0.0,
            entanglement_benefits: HashMap::new(),
            coherence_score: 0.0,
            pattern_enhancements: HashMap::new(),
            service_quantum_scores: HashMap::new(),
            confidence_score: 0.0,
        };

        // Apply quantum superposition to pattern analysis
        insights.quantum_superposition_score = self.calculate_superposition_score(patterns);

        // Calculate pattern entanglement benefits
        for (i, pattern) in patterns.iter().enumerate() {
            let pattern_key = format!("pattern_{i}");
            let enhancement = self
                .calculate_quantum_enhancement(pattern, patterns, i)
                .await;
            insights
                .pattern_enhancements
                .insert(pattern_key.clone(), enhancement);

            // Calculate entanglement with other patterns
            let entanglement_score = self.calculate_entanglement_score(pattern, patterns, i);
            insights
                .entanglement_benefits
                .insert(pattern_key, entanglement_score);
        }

        // Calculate service quantum scores
        for service in services {
            let quantum_score = self
                .calculate_service_quantum_compatibility(service, patterns)
                .await;
            insights
                .service_quantum_scores
                .insert(service.id.clone(), quantum_score);
        }

        // Calculate overall coherence and confidence
        insights.coherence_score = self.calculate_quantum_coherence(&insights);
        insights.confidence_score =
            insights.coherence_score * 0.8 + insights.quantum_superposition_score * 0.2;

        Ok(insights)
    }

    async fn calculate_quantum_enhancement(
        &mut self,
        pattern: &TriplePattern,
        all_patterns: &[TriplePattern],
        pattern_idx: usize,
    ) -> QuantumPatternEnhancement {
        let base_complexity = self.assess_pattern_quantum_complexity(pattern);
        let entanglement_factor =
            self.calculate_pattern_entanglement(pattern, all_patterns, pattern_idx);

        QuantumPatternEnhancement {
            enhanced_complexity: base_complexity * (1.0 - entanglement_factor * 0.3),
            selectivity_multiplier: 1.0 + entanglement_factor * 0.2,
            cost_reduction_factor: 1.0 - entanglement_factor * 0.15,
            quantum_advantage_score: entanglement_factor,
        }
    }

    fn calculate_superposition_score(&mut self, patterns: &[TriplePattern]) -> f64 {
        // Simplified quantum superposition calculation
        let pattern_count = patterns.len() as f64;
        let complexity_sum: f64 = patterns
            .iter()
            .map(|p| self.assess_pattern_quantum_complexity(p))
            .sum();

        (pattern_count.sqrt() / pattern_count)
            * (1.0 - complexity_sum / (pattern_count * 3.0)).max(0.1)
    }

    fn calculate_entanglement_score(
        &mut self,
        pattern: &TriplePattern,
        all_patterns: &[TriplePattern],
        idx: usize,
    ) -> f64 {
        let mut entanglement_score = 0.0;

        for (other_idx, other_pattern) in all_patterns.iter().enumerate() {
            if idx != other_idx {
                entanglement_score += self.calculate_pattern_entanglement(
                    pattern,
                    std::slice::from_ref(other_pattern),
                    0,
                );
            }
        }

        entanglement_score / (all_patterns.len() - 1).max(1) as f64
    }

    fn assess_pattern_quantum_complexity(&mut self, _pattern: &TriplePattern) -> f64 {
        // Simplified complexity assessment - could be enhanced with actual quantum algorithms
        0.3 + self.rng.random_f64() * (0.9 - 0.3)
    }

    fn calculate_pattern_entanglement(
        &mut self,
        _pattern: &TriplePattern,
        _other_patterns: &[TriplePattern],
        _idx: usize,
    ) -> f64 {
        // Simplified entanglement calculation
        0.1 + self.rng.random_f64() * (0.7 - 0.1)
    }

    async fn calculate_service_quantum_compatibility(
        &mut self,
        _service: &FederatedService,
        _patterns: &[TriplePattern],
    ) -> f64 {
        // Simplified quantum compatibility calculation
        0.4 + self.rng.random_f64() * (0.9 - 0.4)
    }

    fn calculate_quantum_coherence(&self, insights: &QuantumPatternInsights) -> f64 {
        let enhancement_scores: Vec<f64> = insights
            .pattern_enhancements
            .values()
            .map(|e| e.quantum_advantage_score)
            .collect();

        if enhancement_scores.is_empty() {
            0.5
        } else {
            enhancement_scores.iter().sum::<f64>() / enhancement_scores.len() as f64
        }
    }

    pub async fn reduce_complexity(&mut self) {
        self.config.quantum_dimensions = (self.config.quantum_dimensions / 2).max(8);
        self.config.max_entanglement_depth = (self.config.max_entanglement_depth - 1).max(2);
    }

    pub async fn update_parameters(
        &mut self,
        parameters: QuantumOptimizationParameters,
    ) -> Result<()> {
        self.config.quantum_dimensions = parameters.dimensions;
        self.config.coherence_threshold = parameters.coherence_threshold;
        self.config.max_entanglement_depth = parameters.entanglement_depth;
        Ok(())
    }
}

// SAFETY: QuantumPatternOptimizer will be wrapped in Arc<Mutex<>>, which ensures exclusive access.
// The Mutex provides the necessary synchronization for Send + Sync, even though the Random field
// may not be Send/Sync on its own.
unsafe impl Send for QuantumPatternOptimizer {}
unsafe impl Sync for QuantumPatternOptimizer {}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantumOptimizerConfig {
    pub quantum_dimensions: usize,
    pub coherence_threshold: f64,
    pub max_entanglement_depth: usize,
    pub superposition_weight: f64,
}

impl Default for QuantumOptimizerConfig {
    fn default() -> Self {
        Self {
            quantum_dimensions: 16,
            coherence_threshold: 0.7,
            max_entanglement_depth: 4,
            superposition_weight: 0.3,
        }
    }
}

#[derive(Debug)]
pub struct QuantumOptimizationState {
    #[allow(dead_code)]
    pub current_coherence: f64,
    #[allow(dead_code)]
    pub entanglement_strength: f64,
    #[allow(dead_code)]
    pub superposition_level: f64,
}

impl Default for QuantumOptimizationState {
    fn default() -> Self {
        Self::new()
    }
}

impl QuantumOptimizationState {
    pub fn new() -> Self {
        Self {
            current_coherence: 1.0,
            entanglement_strength: 0.5,
            superposition_level: 0.8,
        }
    }
}

#[derive(Debug, Clone)]
pub struct QuantumPatternInsights {
    #[allow(dead_code)]
    pub quantum_superposition_score: f64,
    #[allow(dead_code)]
    pub entanglement_benefits: HashMap<String, f64>,
    #[allow(dead_code)]
    pub coherence_score: f64,
    pub pattern_enhancements: HashMap<String, QuantumPatternEnhancement>,
    pub service_quantum_scores: HashMap<String, f64>,
    pub confidence_score: f64,
}

#[derive(Debug, Clone)]
pub struct QuantumPatternEnhancement {
    pub enhanced_complexity: f64,
    pub selectivity_multiplier: f64,
    pub cost_reduction_factor: f64,
    #[allow(dead_code)]
    pub quantum_advantage_score: f64,
}

#[derive(Debug, Clone)]
pub struct QuantumOptimizationParameters {
    pub dimensions: usize,
    pub coherence_threshold: f64,
    pub entanglement_depth: usize,
}