oxirs-arq 0.2.4

//! Real-Time Streaming SPARQL with SciRS2 Signal Processing
//!
//! This module provides cutting-edge real-time SPARQL query processing capabilities
//! for streaming RDF data, leveraging SciRS2's advanced signal processing algorithms
//! for ultra-low latency and high-throughput stream processing.

use crate::algebra::{Algebra, Binding, Solution, Term, TriplePattern, Variable};
use crate::executor::{Dataset, ExecutionContext, QueryExecutor};
use anyhow::Result;
use scirs2_core::array;  // Beta.3 array macro convenience
use scirs2_core::error::CoreError;
// Native SciRS2 APIs (beta.4+)
use scirs2_core::metrics::{Counter, Gauge, Histogram, Timer};
use scirs2_core::profiling::Profiler;
use scirs2_core::ndarray_ext::{Array1, Array2, ArrayView1};
use scirs2_core::parallel_ops::ParallelIterator;
use scirs2_core::random::{
    Rng, Random, seeded_rng, ThreadLocalRngPool, ScientificSliceRandom,
    distributions::{Beta, MultivariateNormal}
};
use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant, SystemTime};
use tokio::sync::{broadcast, mpsc, oneshot};
use tokio::time::interval;

/// Real-time streaming configuration
#[derive(Debug, Clone)]
pub struct StreamingConfig {
    /// Maximum stream buffer size
    pub buffer_size: usize,
    /// Window size for temporal aggregation
    pub window_size: Duration,
    /// Window slide interval
    pub slide_interval: Duration,
    /// Enable signal processing optimizations
    pub enable_signal_processing: bool,
    /// Watermark strategy for late events
    pub watermark_strategy: WatermarkStrategy,
    /// Stream processing parallelism
    pub parallelism: usize,
    /// Enable adaptive sampling
    pub adaptive_sampling: bool,
    /// Signal processing pipeline configuration
    pub signal_pipeline: SignalPipelineConfig,
}

impl Default for StreamingConfig {
    fn default() -> Self {
        Self {
            buffer_size: 100_000,
            window_size: Duration::from_secs(10),
            slide_interval: Duration::from_millis(100),
            enable_signal_processing: true,
            watermark_strategy: WatermarkStrategy::ProcessingTime,
            parallelism: num_cpus::get(),
            adaptive_sampling: true,
            signal_pipeline: SignalPipelineConfig::default(),
        }
    }
}

/// Signal processing pipeline configuration
#[derive(Debug, Clone)]
pub struct SignalPipelineConfig {
    /// Enable Fourier transform analysis
    pub enable_fft: bool,
    /// Enable wavelet decomposition
    pub enable_wavelets: bool,
    /// Noise filtering configuration
    pub noise_filter: NoiseFilterConfig,
    /// Pattern detection configuration
    pub pattern_detection: PatternDetectionConfig,
    /// Anomaly detection thresholds
    pub anomaly_detection: AnomalyDetectionConfig,
}

impl Default for SignalPipelineConfig {
    fn default() -> Self {
        Self {
            enable_fft: true,
            enable_wavelets: true,
            noise_filter: NoiseFilterConfig::default(),
            pattern_detection: PatternDetectionConfig::default(),
            anomaly_detection: AnomalyDetectionConfig::default(),
        }
    }
}

/// Noise filtering configuration
#[derive(Debug, Clone)]
pub struct NoiseFilterConfig {
    /// Enable low-pass filtering
    pub enable_lowpass: bool,
    /// Cutoff frequency for low-pass filter
    pub lowpass_cutoff: f64,
    /// Enable high-pass filtering
    pub enable_highpass: bool,
    /// Cutoff frequency for high-pass filter
    pub highpass_cutoff: f64,
    /// Moving average window size
    pub moving_average_window: usize,
}

impl Default for NoiseFilterConfig {
    fn default() -> Self {
        Self {
            enable_lowpass: true,
            lowpass_cutoff: 0.1,
            enable_highpass: false,
            highpass_cutoff: 0.01,
            moving_average_window: 10,
        }
    }
}

/// Pattern detection configuration
#[derive(Debug, Clone)]
pub struct PatternDetectionConfig {
    /// Enable temporal pattern detection
    pub enable_temporal_patterns: bool,
    /// Enable frequency domain pattern detection
    pub enable_frequency_patterns: bool,
    /// Pattern matching sensitivity
    pub sensitivity: f64,
    /// Minimum pattern duration
    pub min_pattern_duration: Duration,
}

impl Default for PatternDetectionConfig {
    fn default() -> Self {
        Self {
            enable_temporal_patterns: true,
            enable_frequency_patterns: true,
            sensitivity: 0.8,
            min_pattern_duration: Duration::from_millis(500),
        }
    }
}

/// Anomaly detection configuration
#[derive(Debug, Clone)]
pub struct AnomalyDetectionConfig {
    /// Statistical threshold for anomaly detection
    pub statistical_threshold: f64,
    /// Spectral threshold for frequency-based anomalies
    pub spectral_threshold: f64,
    /// Enable adaptive thresholding
    pub adaptive_thresholding: bool,
    /// Anomaly detection window size
    pub detection_window: Duration,
}

impl Default for AnomalyDetectionConfig {
    fn default() -> Self {
        Self {
            statistical_threshold: 3.0, // 3 sigma
            spectral_threshold: 2.5,
            adaptive_thresholding: true,
            detection_window: Duration::from_secs(30),
        }
    }
}

/// Watermark strategies for handling late events
#[derive(Debug, Clone, Copy)]
pub enum WatermarkStrategy {
    /// Use processing time for watermarks
    ProcessingTime,
    /// Use event time for watermarks
    EventTime,
    /// Adaptive watermarks based on stream characteristics
    Adaptive,
}

/// Streaming RDF triple with timestamp
#[derive(Debug, Clone)]
pub struct StreamingTriple {
    /// RDF triple data
    pub subject: Term,
    pub predicate: Term,
    pub object: Term,
    /// Event timestamp
    pub timestamp: SystemTime,
    /// Processing metadata
    pub metadata: TripleMetadata,
}

/// Metadata for streaming triples
#[derive(Debug, Clone)]
pub struct TripleMetadata {
    /// Source stream identifier
    pub source_id: String,
    /// Sequence number in stream
    pub sequence_number: u64,
    /// Quality score (0.0 to 1.0)
    pub quality_score: f64,
    /// Signal strength indicator
    pub signal_strength: f64,
}

/// Windowed query result with temporal bounds
#[derive(Debug, Clone)]
pub struct WindowedResult {
    /// Query solution for this window
    pub solution: Solution,
    /// Window start time
    pub window_start: SystemTime,
    /// Window end time
    pub window_end: SystemTime,
    /// Number of triples processed
    pub triple_count: usize,
    /// Signal processing metrics
    pub signal_metrics: SignalMetrics,
}

/// Signal processing metrics for streaming data
#[derive(Debug, Clone)]
pub struct SignalMetrics {
    /// Average signal strength
    pub avg_signal_strength: f64,
    /// Signal-to-noise ratio
    pub snr: f64,
    /// Dominant frequency components
    pub dominant_frequencies: Vec<f64>,
    /// Detected patterns
    pub patterns: Vec<DetectedPattern>,
    /// Anomaly indicators
    pub anomalies: Vec<AnomalyIndicator>,
}

/// Detected pattern in streaming data
#[derive(Debug, Clone)]
pub struct DetectedPattern {
    /// Pattern type
    pub pattern_type: PatternType,
    /// Pattern strength
    pub strength: f64,
    /// Time range of pattern
    pub time_range: (SystemTime, SystemTime),
    /// Pattern characteristics
    pub characteristics: HashMap<String, f64>,
}

/// Types of patterns detected in streams
#[derive(Debug, Clone)]
pub enum PatternType {
    /// Periodic pattern with frequency
    Periodic(f64),
    /// Trending pattern with direction
    Trending(TrendDirection),
    /// Burst pattern with intensity
    Burst(f64),
    /// Seasonal pattern with period
    Seasonal(Duration),
    /// Custom pattern with identifier
    Custom(String),
}

/// Trend direction for trending patterns
#[derive(Debug, Clone, Copy)]
pub enum TrendDirection {
    Increasing,
    Decreasing,
    Stable,
}

/// Anomaly indicator in streaming data
#[derive(Debug, Clone)]
pub struct AnomalyIndicator {
    /// Anomaly type
    pub anomaly_type: AnomalyType,
    /// Severity score (0.0 to 1.0)
    pub severity: f64,
    /// Detection timestamp
    pub timestamp: SystemTime,
    /// Description of anomaly
    pub description: String,
}

/// Types of anomalies detected in streams
#[derive(Debug, Clone)]
pub enum AnomalyType {
    /// Statistical outlier
    Statistical,
    /// Frequency domain anomaly
    Spectral,
    /// Missing data anomaly
    MissingData,
    /// Quality degradation
    QualityDegradation,
    /// Custom anomaly type
    Custom(String),
}

/// Real-time streaming SPARQL processor
pub struct StreamingSparqlProcessor {
    config: StreamingConfig,
    query_executor: Arc<Mutex<QueryExecutor>>,
    execution_context: ExecutionContext,

    // Stream management
    stream_buffer: Arc<Mutex<VecDeque<StreamingTriple>>>,
    active_windows: Arc<RwLock<HashMap<String, StreamingWindow>>>,
    result_publisher: broadcast::Sender<WindowedResult>,

    // Signal processing components
    signal_processor: StreamProcessor,
    fft_analyzer: FFT,
    wavelet_transformer: WaveletTransform,
    noise_filters: NoiseFilterBank,

    // Performance monitoring
    profiler: Profiler,
    metrics: StreamingMetrics,

    // Background tasks
    processing_task: Option<tokio::task::JoinHandle<()>>,
    watermark_task: Option<tokio::task::JoinHandle<()>>,
}

impl StreamingSparqlProcessor {
    /// Create new streaming SPARQL processor
    pub fn new(config: StreamingConfig) -> Result<Self> {
        let query_executor = Arc::new(Mutex::new(QueryExecutor::new()));
        let execution_context = ExecutionContext::default();

        // Create signal processing components
        let signal_processor = StreamProcessor::new(config.parallelism)?;
        let fft_analyzer = FFT::new(1024)?; // 1024-point FFT
        let wavelet_transformer = WaveletTransform::new(WaveletType::Daubechies4)?;
        let noise_filters = NoiseFilterBank::new(&config.signal_pipeline.noise_filter)?;

        // Create result publisher
        let (result_publisher, _) = broadcast::channel(1000);

        // Initialize metrics
        let metrics = StreamingMetrics::new();

        Ok(Self {
            config,
            query_executor,
            execution_context,
            stream_buffer: Arc::new(Mutex::new(VecDeque::new())),
            active_windows: Arc::new(RwLock::new(HashMap::new())),
            result_publisher,
            signal_processor,
            fft_analyzer,
            wavelet_transformer,
            noise_filters,
            profiler: Profiler::new(),
            metrics,
            processing_task: None,
            watermark_task: None,
        })
    }

    /// Start streaming processing
    pub async fn start(&mut self) -> Result<()> {
        self.profiler.start("streaming_startup");

        // Start background processing task
        let processing_task = self.spawn_processing_task().await?;
        self.processing_task = Some(processing_task);

        // Start watermark management task
        let watermark_task = self.spawn_watermark_task().await?;
        self.watermark_task = Some(watermark_task);

        self.profiler.stop("streaming_startup");
        Ok(())
    }

    /// Stop streaming processing
    pub async fn stop(&mut self) -> Result<()> {
        if let Some(task) = self.processing_task.take() {
            task.abort();
        }

        if let Some(task) = self.watermark_task.take() {
            task.abort();
        }

        Ok(())
    }

    /// Submit streaming triple for processing
    pub async fn submit_triple(&self, triple: StreamingTriple) -> Result<()> {
        self.metrics.triples_received.increment();

        // Apply signal processing if enabled
        let processed_triple = if self.config.enable_signal_processing {
            self.preprocess_triple(triple).await?
        } else {
            triple
        };

        // Add to buffer
        if let Ok(mut buffer) = self.stream_buffer.lock() {
            if buffer.len() >= self.config.buffer_size {
                // Remove oldest triple if buffer is full
                buffer.pop_front();
                self.metrics.triples_dropped.increment();
            }
            buffer.push_back(processed_triple);
        }

        Ok(())
    }

    /// Register continuous SPARQL query
    pub async fn register_query(&self, query_id: String, algebra: Algebra) -> Result<broadcast::Receiver<WindowedResult>> {
        self.profiler.start("query_registration");

        // Create streaming window for this query
        let window = StreamingWindow::new(
            query_id.clone(),
            algebra,
            self.config.window_size,
            self.config.slide_interval,
        )?;

        // Add to active windows
        if let Ok(mut windows) = self.active_windows.write() {
            windows.insert(query_id, window);
        }

        let receiver = self.result_publisher.subscribe();

        self.profiler.stop("query_registration");
        Ok(receiver)
    }

    /// Unregister continuous query
    pub async fn unregister_query(&self, query_id: &str) -> Result<()> {
        if let Ok(mut windows) = self.active_windows.write() {
            windows.remove(query_id);
        }
        Ok(())
    }

    /// Preprocess triple with signal processing
    async fn preprocess_triple(&self, mut triple: StreamingTriple) -> Result<StreamingTriple> {
        // Extract signal from triple metadata
        let signal_value = triple.metadata.signal_strength;

        // Apply noise filtering
        let filtered_signal = self.noise_filters.filter(signal_value)?;

        // Update signal strength
        triple.metadata.signal_strength = filtered_signal;

        // Adaptive quality scoring based on signal characteristics
        if self.config.adaptive_sampling {
            triple.metadata.quality_score = self.calculate_adaptive_quality(&triple).await?;
        }

        Ok(triple)
    }

    /// Calculate adaptive quality score
    async fn calculate_adaptive_quality(&self, triple: &StreamingTriple) -> Result<f64> {
        let base_quality = triple.metadata.quality_score;
        let signal_strength = triple.metadata.signal_strength;

        // Signal strength contribution (0.0 to 1.0)
        let signal_contribution = signal_strength.min(1.0).max(0.0);

        // Temporal freshness contribution
        let now = SystemTime::now();
        let age = now.duration_since(triple.timestamp)
            .unwrap_or(Duration::ZERO)
            .as_secs_f64();

        let freshness_contribution = (-age / 300.0).exp(); // 5-minute half-life

        // Combined quality score
        let adaptive_quality = (base_quality * 0.4 + signal_contribution * 0.3 + freshness_contribution * 0.3)
            .min(1.0)
            .max(0.0);

        Ok(adaptive_quality)
    }

    /// Spawn background processing task
    async fn spawn_processing_task(&self) -> Result<tokio::task::JoinHandle<()>> {
        let stream_buffer = Arc::clone(&self.stream_buffer);
        let active_windows = Arc::clone(&self.active_windows);
        let query_executor = Arc::clone(&self.query_executor);
        let result_publisher = self.result_publisher.clone();
        let config = self.config.clone();
        let metrics = self.metrics.clone();

        let task = tokio::spawn(async move {
            let mut interval = interval(config.slide_interval);

            loop {
                interval.tick().await;

                // Process all active windows
                if let Ok(windows) = active_windows.read() {
                    for (query_id, window) in windows.iter() {
                        // Extract relevant triples from buffer
                        let triples = Self::extract_window_triples(&stream_buffer, window).await;

                        if !triples.is_empty() {
                            // Execute query on windowed data
                            if let Ok(result) = Self::execute_windowed_query(
                                &query_executor,
                                &window.algebra,
                                &triples,
                                &config,
                                &metrics,
                            )
                            .await
                            {
                                // Publish result
                                let _ = result_publisher.send(result);
                            }
                        }
                    }
                }
            }
        });

        Ok(task)
    }

    /// Spawn watermark management task
    async fn spawn_watermark_task(&self) -> Result<tokio::task::JoinHandle<()>> {
        let stream_buffer = Arc::clone(&self.stream_buffer);
        let config = self.config.clone();

        let task = tokio::spawn(async move {
            let mut interval = interval(Duration::from_secs(1));

            loop {
                interval.tick().await;

                // Clean up old triples based on watermark strategy
                Self::apply_watermarks(&stream_buffer, &config).await;
            }
        });

        Ok(task)
    }

    /// Extract triples for a specific window
    async fn extract_window_triples(
        stream_buffer: &Arc<Mutex<VecDeque<StreamingTriple>>>,
        window: &StreamingWindow,
    ) -> Vec<StreamingTriple> {
        if let Ok(buffer) = stream_buffer.lock() {
            let now = SystemTime::now();
            let window_start = now - window.window_size;

            buffer
                .iter()
                .filter(|triple| triple.timestamp >= window_start && triple.timestamp <= now)
                .cloned()
                .collect()
        } else {
            Vec::new()
        }
    }

    /// Execute query on windowed data
    async fn execute_windowed_query(
        query_executor: &Arc<Mutex<QueryExecutor>>,
        algebra: &Algebra,
        triples: &[StreamingTriple],
        config: &StreamingConfig,
        metrics: &StreamingMetrics,
    ) -> Result<WindowedResult> {
        let start_time = Instant::now();

        // Convert streaming triples to in-memory dataset
        let dataset = StreamingDataset::from_triples(triples);

        // Execute query
        let solution = if let Ok(mut executor) = query_executor.lock() {
            let (sol, _stats) = executor.execute(algebra, &dataset)?;
            sol
        } else {
            Solution::new()
        };

        // Calculate signal metrics
        let signal_metrics = Self::calculate_signal_metrics(triples, config).await?;

        let execution_time = start_time.elapsed();
        metrics.query_execution_time.observe(execution_time);

        let now = SystemTime::now();
        Ok(WindowedResult {
            solution,
            window_start: now - config.window_size,
            window_end: now,
            triple_count: triples.len(),
            signal_metrics,
        })
    }

    /// Calculate signal processing metrics for window
    async fn calculate_signal_metrics(
        triples: &[StreamingTriple],
        config: &StreamingConfig,
    ) -> Result<SignalMetrics> {
        if triples.is_empty() {
            return Ok(SignalMetrics {
                avg_signal_strength: 0.0,
                snr: 0.0,
                dominant_frequencies: Vec::new(),
                patterns: Vec::new(),
                anomalies: Vec::new(),
            });
        }

        // Extract signal values
        let signal_values: Array1<f64> = Array1::from_vec(
            triples
                .iter()
                .map(|t| t.metadata.signal_strength)
                .collect()
        );

        let avg_signal_strength = signal_values.mean().unwrap_or(0.0);

        // Calculate signal-to-noise ratio
        let signal_variance = signal_values.var(0.0);
        let snr = if signal_variance > 0.0 {
            avg_signal_strength / signal_variance.sqrt()
        } else {
            0.0
        };

        // Frequency analysis if enabled
        let dominant_frequencies = if config.signal_pipeline.enable_fft && signal_values.len() >= 8 {
            Self::analyze_frequencies(&signal_values).await?
        } else {
            Vec::new()
        };

        // Pattern detection
        let patterns = if config.signal_pipeline.pattern_detection.enable_temporal_patterns {
            Self::detect_patterns(&signal_values, &config.signal_pipeline.pattern_detection).await?
        } else {
            Vec::new()
        };

        // Anomaly detection
        let anomalies = Self::detect_anomalies(&signal_values, &config.signal_pipeline.anomaly_detection).await?;

        Ok(SignalMetrics {
            avg_signal_strength,
            snr,
            dominant_frequencies,
            patterns,
            anomalies,
        })
    }

    /// Analyze frequency components in signal
    async fn analyze_frequencies(signal: &Array1<f64>) -> Result<Vec<f64>> {
        // Simplified frequency analysis
        let mut frequencies = Vec::new();

        // Find peaks in frequency domain (simplified approach)
        let mean = signal.mean().unwrap_or(0.0);
        let mut above_mean_count = 0;

        for &value in signal.iter() {
            if value > mean {
                above_mean_count += 1;
            }
        }

        if above_mean_count > 0 {
            let dominant_freq = above_mean_count as f64 / signal.len() as f64;
            frequencies.push(dominant_freq);
        }

        Ok(frequencies)
    }

    /// Detect patterns in signal
    async fn detect_patterns(
        signal: &Array1<f64>,
        config: &PatternDetectionConfig,
    ) -> Result<Vec<DetectedPattern>> {
        let mut patterns = Vec::new();

        if signal.len() < 10 {
            return Ok(patterns);
        }

        // Simple trend detection
        let first_half = &signal.slice(s![..signal.len() / 2]);
        let second_half = &signal.slice(s![signal.len() / 2..]);

        let first_mean = first_half.mean().unwrap_or(0.0);
        let second_mean = second_half.mean().unwrap_or(0.0);

        let trend_strength = ((second_mean - first_mean) / first_mean.max(1e-10)).abs();

        if trend_strength > config.sensitivity {
            let trend_direction = if second_mean > first_mean {
                TrendDirection::Increasing
            } else if second_mean < first_mean {
                TrendDirection::Decreasing
            } else {
                TrendDirection::Stable
            };

            let now = SystemTime::now();
            patterns.push(DetectedPattern {
                pattern_type: PatternType::Trending(trend_direction),
                strength: trend_strength,
                time_range: (now - Duration::from_secs(30), now),
                characteristics: {
                    let mut chars = HashMap::new();
                    chars.insert("trend_magnitude".to_string(), trend_strength);
                    chars.insert("first_half_mean".to_string(), first_mean);
                    chars.insert("second_half_mean".to_string(), second_mean);
                    chars
                },
            });
        }

        Ok(patterns)
    }

    /// Detect anomalies in signal
    async fn detect_anomalies(
        signal: &Array1<f64>,
        config: &AnomalyDetectionConfig,
    ) -> Result<Vec<AnomalyIndicator>> {
        let mut anomalies = Vec::new();

        if signal.is_empty() {
            return Ok(anomalies);
        }

        let mean = signal.mean().unwrap_or(0.0);
        let std_dev = signal.std(0.0);

        // Statistical anomaly detection
        for (i, &value) in signal.iter().enumerate() {
            let z_score = if std_dev > 0.0 {
                (value - mean) / std_dev
            } else {
                0.0
            };

            if z_score.abs() > config.statistical_threshold {
                anomalies.push(AnomalyIndicator {
                    anomaly_type: AnomalyType::Statistical,
                    severity: (z_score.abs() - config.statistical_threshold) / config.statistical_threshold,
                    timestamp: SystemTime::now(),
                    description: format!("Statistical outlier at position {i} with z-score {z_score:.2}"),
                });
            }
        }

        Ok(anomalies)
    }

    /// Apply watermarks to clean up old data
    async fn apply_watermarks(
        stream_buffer: &Arc<Mutex<VecDeque<StreamingTriple>>>,
        config: &StreamingConfig,
    ) {
        if let Ok(mut buffer) = stream_buffer.lock() {
            let watermark_time = match config.watermark_strategy {
                WatermarkStrategy::ProcessingTime => SystemTime::now() - config.window_size * 2,
                WatermarkStrategy::EventTime => SystemTime::now() - config.window_size * 3,
                WatermarkStrategy::Adaptive => SystemTime::now() - config.window_size,
            };

            // Remove triples older than watermark
            while let Some(triple) = buffer.front() {
                if triple.timestamp < watermark_time {
                    buffer.pop_front();
                } else {
                    break;
                }
            }
        }
    }

    /// Get comprehensive streaming statistics
    pub fn get_statistics(&self) -> StreamingStatistics {
        StreamingStatistics {
            triples_received: self.metrics.triples_received.get(),
            triples_processed: self.metrics.triples_processed.get(),
            triples_dropped: self.metrics.triples_dropped.get(),
            active_queries: self.active_windows.read().map(|w| w.len()).unwrap_or(0),
            avg_processing_latency: self.metrics.processing_latency.mean(),
            avg_query_execution_time: self.metrics.query_execution_time.mean(),
            buffer_utilization: self.calculate_buffer_utilization(),
            signal_quality_avg: self.metrics.signal_quality.mean(),
        }
    }

    /// Calculate buffer utilization percentage
    fn calculate_buffer_utilization(&self) -> f64 {
        if let Ok(buffer) = self.stream_buffer.lock() {
            (buffer.len() as f64 / self.config.buffer_size as f64) * 100.0
        } else {
            0.0
        }
    }
}

/// Streaming window for continuous queries
#[derive(Debug, Clone)]
struct StreamingWindow {
    pub query_id: String,
    pub algebra: Algebra,
    pub window_size: Duration,
    pub slide_interval: Duration,
    pub last_processed: SystemTime,
}

impl StreamingWindow {
    fn new(
        query_id: String,
        algebra: Algebra,
        window_size: Duration,
        slide_interval: Duration,
    ) -> Result<Self> {
        Ok(Self {
            query_id,
            algebra,
            window_size,
            slide_interval,
            last_processed: SystemTime::now(),
        })
    }
}

/// In-memory dataset for streaming triples
struct StreamingDataset {
    triples: Vec<(Term, Term, Term)>,
}

impl StreamingDataset {
    fn from_triples(streaming_triples: &[StreamingTriple]) -> Self {
        let triples = streaming_triples
            .iter()
            .map(|st| (st.subject.clone(), st.predicate.clone(), st.object.clone()))
            .collect();

        Self { triples }
    }
}

impl Dataset for StreamingDataset {
    fn find_triples(&self, pattern: &TriplePattern) -> Result<Vec<(Term, Term, Term)>> {
        let matches = self
            .triples
            .iter()
            .filter(|(s, p, o)| {
                let subject_matches = match &pattern.subject {
                    Term::Variable(_) => true,
                    term => term == s,
                };

                let predicate_matches = match &pattern.predicate {
                    Term::Variable(_) => true,
                    term => term == p,
                };

                let object_matches = match &pattern.object {
                    Term::Variable(_) => true,
                    term => term == o,
                };

                subject_matches && predicate_matches && object_matches
            })
            .cloned()
            .collect();

        Ok(matches)
    }
}

/// Noise filter bank for signal processing
struct NoiseFilterBank {
    moving_average: MovingAverageFilter,
    butterworth: Option<ButterWorthFilter>,
}

impl NoiseFilterBank {
    fn new(config: &NoiseFilterConfig) -> Result<Self> {
        let moving_average = MovingAverageFilter::new(config.moving_average_window)?;

        let butterworth = if config.enable_lowpass {
            Some(ButterWorthFilter::lowpass(config.lowpass_cutoff, 2)?)
        } else {
            None
        };

        Ok(Self {
            moving_average,
            butterworth,
        })
    }

    fn filter(&self, value: f64) -> Result<f64> {
        let mut filtered = value;

        // Apply moving average
        filtered = self.moving_average.filter(filtered)?;

        // Apply Butterworth filter if enabled
        if let Some(ref butterworth) = self.butterworth {
            filtered = butterworth.filter(filtered)?;
        }

        Ok(filtered)
    }
}

/// Performance metrics for streaming processing
#[derive(Debug, Clone)]
struct StreamingMetrics {
    triples_received: Counter,
    triples_processed: Counter,
    triples_dropped: Counter,
    processing_latency: Histogram,
    query_execution_time: Timer,
    signal_quality: Histogram,
}

impl StreamingMetrics {
    fn new() -> Self {
        Self {
            triples_received: Counter::new("triples_received".to_string()),
            triples_processed: Counter::new("triples_processed".to_string()),
            triples_dropped: Counter::new("triples_dropped".to_string()),
            processing_latency: Histogram::new("processing_latency".to_string()),
            query_execution_time: Timer::new("query_execution_time".to_string()),
            signal_quality: Histogram::new("signal_quality".to_string()),
        }
    }
}

/// Comprehensive streaming statistics
#[derive(Debug, Clone)]
pub struct StreamingStatistics {
    pub triples_received: u64,
    pub triples_processed: u64,
    pub triples_dropped: u64,
    pub active_queries: usize,
    pub avg_processing_latency: Duration,
    pub avg_query_execution_time: Duration,
    pub buffer_utilization: f64,
    pub signal_quality_avg: f64,
}

#[cfg(test)]
mod tests {
    use super::*;
    use oxirs_core::model::NamedNode;

    #[tokio::test]
    async fn test_streaming_processor_creation() {
        let config = StreamingConfig::default();
        let processor = StreamingSparqlProcessor::new(config);
        assert!(processor.is_ok());
    }

    #[tokio::test]
    async fn test_streaming_triple_submission() {
        let config = StreamingConfig::default();
        let processor = StreamingSparqlProcessor::new(config).unwrap();

        let triple = StreamingTriple {
            subject: Term::Iri(NamedNode::new("http://example.org/subject").unwrap()),
            predicate: Term::Iri(NamedNode::new("http://example.org/predicate").unwrap()),
            object: Term::Iri(NamedNode::new("http://example.org/object").unwrap()),
            timestamp: SystemTime::now(),
            metadata: TripleMetadata {
                source_id: "test_stream".to_string(),
                sequence_number: 1,
                quality_score: 0.9,
                signal_strength: 0.8,
            },
        };

        assert!(processor.submit_triple(triple).await.is_ok());
    }

    #[tokio::test]
    async fn test_query_registration() {
        let config = StreamingConfig::default();
        let processor = StreamingSparqlProcessor::new(config).unwrap();

        let algebra = Algebra::Bgp(vec![TriplePattern {
            subject: Term::Variable(Variable::new("s")),
            predicate: Term::Variable(Variable::new("p")),
            object: Term::Variable(Variable::new("o")),
        }]);

        let receiver = processor.register_query("test_query".to_string(), algebra).await;
        assert!(receiver.is_ok());
    }

    #[test]
    fn test_signal_processing_config() {
        let config = SignalPipelineConfig::default();
        assert!(config.enable_fft);
        assert!(config.enable_wavelets);
    }

    #[test]
    fn test_noise_filter_bank() {
        let config = NoiseFilterConfig::default();
        let filter_bank = NoiseFilterBank::new(&config);
        assert!(filter_bank.is_ok());

        let filtered = filter_bank.unwrap().filter(1.0);
        assert!(filtered.is_ok());
    }

    #[test]
    fn test_pattern_detection_config() {
        let config = PatternDetectionConfig::default();
        assert!(config.enable_temporal_patterns);
        assert!(config.enable_frequency_patterns);
        assert_eq!(config.sensitivity, 0.8);
    }
}