oxirs_federate/query_decomposition/

advanced_pattern_analysis_consciousness.rs

//! Consciousness-based and neural pattern analysis components.
//!
//! This sub-module contains:
//! - `ConsciousnessPatternEngine` for deep query pattern introspection
//! - `NeuralPerformancePredictor` for ML-driven performance estimation
//! - `AdaptivePatternCache` for intelligent result caching
//! - Supporting configuration and result types

use anyhow::Result;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::time::Duration;

use crate::{
    planner::planning::{FilterExpression, TriplePattern},
    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(())
    }
}

/// 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, Serialize, 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, Serialize, 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, Serialize, 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(),
        }
    }
}