oxirs_shacl_ai/advanced_pattern_mining/

engine.rs

//! Main pattern mining engine implementation

use std::collections::HashMap;
use tracing::{debug, info, warn};

use oxirs_core::Store;

use super::algorithms::*;
use super::cache::IntelligentPatternCache;
use super::patterns::*;
use super::sparql::*;
use super::types::*;
use crate::Result;

/// Advanced pattern mining engine
#[derive(Debug)]
pub struct AdvancedPatternMiningEngine {
    /// Configuration
    config: AdvancedPatternMiningConfig,

    /// Mining statistics
    stats: PatternMiningStats,

    /// Enhanced pattern cache with intelligent management
    pattern_cache: IntelligentPatternCache,

    /// Frequency tables for different item types
    frequency_tables: FrequencyTables,
}

/// Frequency tables for pattern mining
#[derive(Debug, Default)]
pub struct FrequencyTables {
    /// Property frequency table
    pub properties: HashMap<String, usize>,

    /// Class frequency table
    pub classes: HashMap<String, usize>,

    /// Value pattern frequency table
    pub value_patterns: HashMap<String, usize>,

    /// Co-occurrence matrix
    pub co_occurrence: HashMap<(String, String), usize>,
}

impl AdvancedPatternMiningEngine {
    /// Create new pattern mining engine
    pub fn new() -> Self {
        Self::with_config(AdvancedPatternMiningConfig::default())
    }

    /// Create pattern mining engine with configuration
    pub fn with_config(config: AdvancedPatternMiningConfig) -> Self {
        Self {
            config,
            stats: PatternMiningStats::default(),
            pattern_cache: IntelligentPatternCache::new(),
            frequency_tables: FrequencyTables::default(),
        }
    }

    /// Mine patterns from RDF store
    pub fn mine_patterns(
        &mut self,
        store: &dyn Store,
        graph_name: Option<&str>,
    ) -> Result<Vec<AdvancedPattern>> {
        let start_time = std::time::Instant::now();
        info!("Starting advanced pattern mining");

        // Build frequency tables
        self.build_frequency_tables(store, graph_name)?;

        // Discover frequent itemsets
        let frequent_itemsets = self.discover_frequent_itemsets()?;
        debug!("Found {} frequent itemsets", frequent_itemsets.len());

        // Generate association rules
        let mut patterns = self.generate_association_rules(&frequent_itemsets)?;
        debug!("Generated {} association rules", patterns.len());

        // Enhance with temporal analysis if enabled
        if self.config.enable_temporal_analysis {
            self.enhance_with_temporal_analysis(&mut patterns, store, graph_name)?;
        }

        // Add hierarchical information if enabled
        if self.config.enable_hierarchical_patterns {
            self.analyze_hierarchical_patterns(&mut patterns)?;
        }

        // Generate SHACL constraint suggestions
        self.generate_constraint_suggestions(&mut patterns)?;

        // Filter by quality threshold
        patterns.retain(|p| p.quality_score >= self.config.quality_threshold);

        // Update statistics
        self.stats.total_patterns = patterns.len();
        self.stats.high_quality_patterns =
            patterns.iter().filter(|p| p.quality_score >= 0.9).count();
        self.stats.temporal_patterns = patterns
            .iter()
            .filter(|p| p.temporal_info.is_some())
            .count();
        self.stats.hierarchical_patterns =
            patterns.iter().filter(|p| p.hierarchy_level > 0).count();
        self.stats.processing_time_ms = start_time.elapsed().as_millis() as u64;

        info!(
            "Pattern mining completed: {} patterns found in {}ms",
            patterns.len(),
            self.stats.processing_time_ms
        );

        Ok(patterns)
    }

    /// Get mining statistics
    pub fn get_stats(&self) -> &PatternMiningStats {
        &self.stats
    }

    /// Get configuration
    pub fn get_config(&self) -> &AdvancedPatternMiningConfig {
        &self.config
    }

    /// Update configuration
    pub fn update_config(&mut self, config: AdvancedPatternMiningConfig) {
        self.config = config;
    }

    /// Get frequency tables
    pub fn get_frequency_tables(&self) -> &FrequencyTables {
        &self.frequency_tables
    }

    /// Clear cache
    pub fn clear_cache(&mut self) {
        self.pattern_cache.clear();
    }

    /// Get cache statistics
    pub fn get_cache_stats(&self) -> crate::Result<serde_json::Value> {
        self.pattern_cache.get_stats()
    }

    /// Warm cache with frequently accessed patterns
    pub fn warm_cache(&mut self) -> usize {
        // Placeholder implementation for cache warming
        0
    }

    /// Get cache analytics
    pub fn get_cache_analytics(&self) -> serde_json::Value {
        self.get_cache_stats().unwrap_or_default()
    }

    /// Get advanced cache statistics
    pub fn get_advanced_cache_statistics(&self) -> serde_json::Value {
        self.get_cache_stats().unwrap_or_default()
    }

    /// Evaluate cache strategy
    pub fn evaluate_cache_strategy(&self) -> bool {
        // Placeholder implementation for cache strategy evaluation
        false
    }

    /// Get cache recommendations
    pub fn get_cache_recommendations(&self) -> Vec<String> {
        // Placeholder implementation for cache recommendations
        vec![]
    }

    /// Get cache eviction strategy
    pub fn get_cache_eviction_strategy(&self) -> String {
        // Placeholder implementation for cache eviction strategy
        "LRU".to_string()
    }

    /// Mine sequential patterns
    pub fn mine_sequential_patterns(
        &mut self,
        store: &dyn Store,
        graph_name: Option<&str>,
        _min_support: f64,
    ) -> crate::Result<Vec<AdvancedPattern>> {
        // Placeholder implementation for sequential pattern mining
        self.mine_patterns(store, graph_name)
    }

    /// Mine graph patterns
    pub fn mine_graph_patterns(
        &mut self,
        store: &dyn Store,
        graph_name: Option<&str>,
        _max_size: usize,
    ) -> crate::Result<Vec<AdvancedPattern>> {
        // Placeholder implementation for graph pattern mining
        self.mine_patterns(store, graph_name)
    }

    /// Mine enhanced temporal patterns
    pub fn mine_enhanced_temporal_patterns(
        &mut self,
        store: &dyn Store,
        graph_name: Option<&str>,
        _granularity: crate::advanced_pattern_mining::TimeGranularity,
    ) -> crate::Result<Vec<AdvancedPattern>> {
        // Placeholder implementation for enhanced temporal pattern mining
        self.mine_patterns(store, graph_name)
    }

    /// Rank patterns with advanced criteria
    pub fn rank_patterns_advanced(
        &self,
        patterns: &mut [AdvancedPattern],
        _criteria: &crate::advanced_pattern_mining::PatternRankingCriteria,
    ) -> Vec<f64> {
        // Placeholder implementation for advanced pattern ranking
        patterns.iter().map(|p| p.quality_score).collect()
    }

    /// Perform enhanced statistical analysis
    pub fn perform_enhanced_statistical_analysis(
        &self,
        patterns: &[AdvancedPattern],
    ) -> serde_json::Value {
        // Placeholder implementation for enhanced statistical analysis
        serde_json::json!({
            "total_patterns": patterns.len(),
            "average_quality": patterns.iter().map(|p| p.quality_score).sum::<f64>() / patterns.len() as f64
        })
    }

    /// Get cached patterns
    pub fn get_cached_patterns(&self, _cache_key: &str) -> Option<Vec<AdvancedPattern>> {
        // Placeholder implementation for getting cached patterns
        None
    }

    /// Cache patterns
    pub fn cache_patterns(&mut self, _cache_key: String, _patterns: Vec<AdvancedPattern>) {
        // Placeholder implementation for caching patterns
    }

    /// Build frequency tables from store data
    fn build_frequency_tables(
        &mut self,
        store: &dyn Store,
        graph_name: Option<&str>,
    ) -> Result<()> {
        debug!("Building frequency tables from RDF store");

        // Enhanced frequency analysis with real SPARQL queries
        self.analyze_property_frequencies(store, graph_name)?;
        self.analyze_class_frequencies(store, graph_name)?;
        self.analyze_value_pattern_frequencies(store, graph_name)?;
        self.build_co_occurrence_matrix(store, graph_name)?;

        debug!(
            "Built frequency tables: {} properties, {} classes, {} value patterns",
            self.frequency_tables.properties.len(),
            self.frequency_tables.classes.len(),
            self.frequency_tables.value_patterns.len()
        );

        Ok(())
    }

    /// Analyze property usage frequencies
    fn analyze_property_frequencies(
        &mut self,
        store: &dyn Store,
        graph_name: Option<&str>,
    ) -> Result<()> {
        debug!("Analyzing property frequencies");

        // SPARQL query to count property usage
        let query = r#"
            SELECT ?property (COUNT(*) as ?count) WHERE {
                ?subject ?property ?object .
            } GROUP BY ?property
            ORDER BY DESC(?count)
        "#;

        // Execute query and process results
        match execute_sparql_query(store, query, graph_name) {
            Ok(results) => {
                process_property_frequency_results(&mut self.frequency_tables, results)?;
            }
            Err(e) => {
                warn!(
                    "Failed to execute property frequency query: {}, using fallback analysis",
                    e
                );
                fallback_property_analysis(&mut self.frequency_tables, store, graph_name)?;
            }
        }

        Ok(())
    }

    /// Analyze class usage frequencies
    fn analyze_class_frequencies(
        &mut self,
        store: &dyn Store,
        graph_name: Option<&str>,
    ) -> Result<()> {
        debug!("Analyzing class frequencies");

        let query = r#"
            SELECT ?class (COUNT(DISTINCT ?instance) as ?count) WHERE {
                ?instance a ?class .
            } GROUP BY ?class
            ORDER BY DESC(?count)
        "#;

        match execute_sparql_query(store, query, graph_name) {
            Ok(results) => {
                process_class_frequency_results(&mut self.frequency_tables, results)?;
            }
            Err(e) => {
                warn!(
                    "Failed to execute class frequency query: {}, using fallback analysis",
                    e
                );
                fallback_class_analysis(&mut self.frequency_tables, store, graph_name)?;
            }
        }

        Ok(())
    }

    /// Analyze value pattern frequencies
    fn analyze_value_pattern_frequencies(
        &mut self,
        store: &dyn Store,
        graph_name: Option<&str>,
    ) -> Result<()> {
        debug!("Analyzing value pattern frequencies");

        let query = r#"
            SELECT ?pattern (COUNT(*) as ?count) WHERE {
                ?subject ?property ?object .
                BIND(REPLACE(STR(?object), "^(.*?)(\\d+|[a-zA-Z]+).*$", "$2") AS ?pattern)
                FILTER(STRLEN(?pattern) > 0)
            } GROUP BY ?pattern
            HAVING (?count > 10)
            ORDER BY DESC(?count)
        "#;

        match execute_sparql_query(store, query, graph_name) {
            Ok(results) => {
                process_value_pattern_results(&mut self.frequency_tables, results)?;
            }
            Err(e) => {
                warn!(
                    "Failed to execute value pattern query: {}, using fallback analysis",
                    e
                );
                fallback_value_pattern_analysis(&mut self.frequency_tables, store, graph_name)?;
            }
        }

        Ok(())
    }

    /// Build co-occurrence matrix for pattern analysis
    fn build_co_occurrence_matrix(
        &mut self,
        store: &dyn Store,
        graph_name: Option<&str>,
    ) -> Result<()> {
        debug!("Building co-occurrence matrix");

        let query = r#"
            SELECT ?prop1 ?prop2 (COUNT(*) as ?count) WHERE {
                ?subject ?prop1 ?obj1 .
                ?subject ?prop2 ?obj2 .
                FILTER(?prop1 != ?prop2)
            } GROUP BY ?prop1 ?prop2
            HAVING (?count > 5)
            ORDER BY DESC(?count)
        "#;

        match execute_sparql_query(store, query, graph_name) {
            Ok(results) => {
                process_co_occurrence_results(&mut self.frequency_tables, results)?;
            }
            Err(e) => {
                warn!(
                    "Failed to execute co-occurrence query: {}, using fallback analysis",
                    e
                );
                fallback_co_occurrence_analysis(&mut self.frequency_tables, store, graph_name)?;
            }
        }

        Ok(())
    }

    /// Discover frequent itemsets using Apriori algorithm
    fn discover_frequent_itemsets(&self) -> Result<Vec<Vec<String>>> {
        discover_frequent_itemsets(&self.frequency_tables, &self.config)
    }

    /// Generate association rules from frequent itemsets
    fn generate_association_rules(
        &self,
        frequent_itemsets: &[Vec<String>],
    ) -> Result<Vec<AdvancedPattern>> {
        generate_association_rules(frequent_itemsets, &self.frequency_tables, &self.config)
    }

    /// Enhance patterns with temporal analysis
    fn enhance_with_temporal_analysis(
        &self,
        patterns: &mut [AdvancedPattern],
        store: &dyn Store,
        graph_name: Option<&str>,
    ) -> Result<()> {
        enhance_with_temporal_analysis(patterns, store, graph_name, &self.config)
    }

    /// Analyze hierarchical patterns
    fn analyze_hierarchical_patterns(&self, patterns: &mut [AdvancedPattern]) -> Result<()> {
        analyze_hierarchical_patterns(patterns, &self.frequency_tables, &self.config)
    }

    /// Generate SHACL constraint suggestions
    fn generate_constraint_suggestions(&self, patterns: &mut [AdvancedPattern]) -> Result<()> {
        generate_constraint_suggestions(patterns, &self.config)
    }
}

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