399 lines
12 KiB
Markdown
399 lines
12 KiB
Markdown
# Temporal-Compare Integration Strategy
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## Executive Summary
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This document outlines the integration strategy for the `temporal-compare` crate into the Lean Agentic Learning System. Temporal-compare provides advanced temporal sequence comparison and pattern matching capabilities essential for analyzing time-series data in streaming contexts.
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## Research Background
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### Temporal Sequence Analysis
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**Definition**: Temporal sequence analysis involves comparing sequences of events or states over time to identify patterns, anomalies, and causal relationships.
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**Key Concepts**:
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1. **Dynamic Time Warping (DTW)** [1]: Algorithm for measuring similarity between temporal sequences
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2. **Longest Common Subsequence (LCS)** [2]: Finding maximal subsequences common to multiple sequences
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3. **Edit Distance** [3]: Measuring dissimilarity between sequences
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4. **Temporal Pattern Mining** [4]: Discovering recurring patterns in time-series data
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### References
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[1] Sakoe, H., & Chiba, S. (1978). "Dynamic programming algorithm optimization for spoken word recognition." IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43-49.
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[2] Bergroth, L., Hakonen, H., & Raita, T. (2000). "A survey of longest common subsequence algorithms." Proceedings of SPIRE 2000, 39-48.
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[3] Levenshtein, V. I. (1966). "Binary codes capable of correcting deletions, insertions, and reversals." Soviet Physics Doklady, 10(8), 707-710.
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[4] Agrawal, R., & Srikant, R. (1995). "Mining sequential patterns." Proceedings of ICDE '95, 3-14.
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## Integration Architecture
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```
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┌─────────────────────────────────────────────────────────────┐
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│ Lean Agentic Learning System │
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├─────────────────────────────────────────────────────────────┤
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│ │
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│ ┌──────────────┐ ┌──────────────────┐ │
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│ │ Knowledge │ │ Temporal │ │
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│ │ Graph │◄───────►│ Compare │ │
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│ │ │ │ Engine │ │
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│ └──────────────┘ └──────────────────┘ │
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│ │ │ │
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│ │ │ │
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│ ┌──────▼──────┐ ┌───────▼──────────┐ │
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│ │ Stream │ │ Pattern │ │
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│ │ Learning │◄────────►│ Detection │ │
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│ └─────────────┘ └──────────────────┘ │
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│ │
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└─────────────────────────────────────────────────────────────┘
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```
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## Use Cases
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### 1. Conversation Flow Analysis
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**Problem**: Identify similar conversation patterns across different user sessions.
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**Solution**: Use temporal-compare to find conversations with similar question-answer sequences.
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**Implementation**:
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```rust
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// Compare two conversation flows
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let conversation1 = extract_conversation_sequence(session1);
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let conversation2 = extract_conversation_sequence(session2);
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let similarity = temporal_compare::dtw_distance(
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&conversation1,
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&conversation2,
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similarity_metric
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);
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if similarity < threshold {
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// Apply learned patterns from conversation1 to conversation2
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}
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```
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### 2. Intent Trajectory Matching
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**Problem**: Predict user intent based on historical intent sequences.
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**Solution**: Match current intent sequence against historical patterns.
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**Implementation**:
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```rust
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let current_intents = vec![Intent::Weather, Intent::Calendar];
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let historical_patterns = load_intent_patterns();
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let best_match = temporal_compare::find_best_match(
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¤t_intents,
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&historical_patterns
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);
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predict_next_intent(best_match);
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```
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### 3. Anomaly Detection in Agent Behavior
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**Problem**: Detect unusual agent decision sequences that deviate from learned patterns.
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**Solution**: Compare current decision sequence against baseline.
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**Implementation**:
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```rust
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let current_actions = agent.get_action_history();
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let baseline_sequence = get_baseline_pattern();
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let edit_distance = temporal_compare::edit_distance(
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¤t_actions,
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&baseline_sequence
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);
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if edit_distance > anomaly_threshold {
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trigger_anomaly_alert();
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}
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```
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## Technical Specifications
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### API Design
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```rust
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pub struct TemporalComparator<T> {
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sequences: Vec<Sequence<T>>,
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cache: LruCache<SequencePair, f64>,
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}
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pub enum ComparisonAlgorithm {
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DTW, // Dynamic Time Warping
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LCS, // Longest Common Subsequence
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EditDistance, // Levenshtein distance
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Correlation, // Cross-correlation
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}
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impl<T: Clone + PartialEq> TemporalComparator<T> {
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pub fn compare(
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&mut self,
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seq1: &[T],
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seq2: &[T],
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algorithm: ComparisonAlgorithm,
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) -> f64;
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pub fn find_similar(
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&self,
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query: &[T],
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threshold: f64,
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) -> Vec<(usize, f64)>;
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pub fn detect_pattern(
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&self,
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sequence: &[T],
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pattern: &[T],
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) -> Vec<usize>;
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}
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```
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### Performance Requirements
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| Operation | Target | Rationale |
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|-----------|--------|-----------|
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| DTW (n=100) | <10ms | Real-time comparison |
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| LCS (n=100) | <5ms | Fast pattern matching |
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| Pattern search | <50ms | Interactive response |
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| Cache hit rate | >80% | Reduce recomputation |
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## Integration Points
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### 1. Knowledge Graph Enhancement
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**Location**: `src/lean_agentic/knowledge.rs`
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**Enhancement**:
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```rust
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impl KnowledgeGraph {
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pub async fn find_similar_entities_temporal(
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&self,
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entity_sequence: &[Entity],
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) -> Vec<Vec<Entity>> {
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// Use temporal-compare to find similar entity sequences
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}
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}
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```
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### 2. Agent Decision History
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**Location**: `src/lean_agentic/agent.rs`
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**Enhancement**:
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```rust
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impl AgenticLoop {
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pub async fn find_similar_decision_sequences(
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&self,
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current_sequence: &[Action],
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) -> Vec<(Vec<Action>, f64)> {
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// Use temporal-compare to find similar past decisions
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}
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}
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```
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### 3. Stream Pattern Detection
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**Location**: `src/lean_agentic/learning.rs`
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**Enhancement**:
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```rust
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impl StreamLearner {
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pub async fn detect_recurring_patterns(
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&self,
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min_support: f64,
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) -> Vec<Pattern> {
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// Use temporal-compare to mine frequent patterns
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}
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}
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```
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## Implementation Phases
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### Phase 1: Core Integration (Week 1)
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- [ ] Add temporal-compare dependency
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- [ ] Create wrapper types for sequences
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- [ ] Implement basic DTW and LCS
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- [ ] Add caching layer
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- [ ] Write unit tests
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### Phase 2: Pattern Mining (Week 2)
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- [ ] Implement pattern detection
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- [ ] Add pattern storage
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- [ ] Create pattern matching API
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- [ ] Integrate with knowledge graph
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- [ ] Write integration tests
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### Phase 3: Optimization (Week 3)
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- [ ] Profile performance
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- [ ] Optimize hot paths
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- [ ] Add SIMD acceleration
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- [ ] Implement parallel processing
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- [ ] Benchmark against baseline
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### Phase 4: Advanced Features (Week 4)
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- [ ] Add streaming DTW
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- [ ] Implement incremental LCS
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- [ ] Create pattern templates
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- [ ] Add confidence scoring
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- [ ] Write documentation
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## Benchmarking Strategy
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### Benchmark Suite
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```rust
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#[bench]
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fn bench_dtw_small(b: &mut Bencher) {
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let seq1 = generate_sequence(50);
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let seq2 = generate_sequence(50);
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b.iter(|| {
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temporal_compare::dtw(&seq1, &seq2)
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});
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}
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#[bench]
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fn bench_pattern_detection(b: &mut Bencher) {
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let sequences = generate_sequences(100, 100);
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let pattern = generate_pattern(10);
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b.iter(|| {
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temporal_compare::detect_pattern(&sequences, &pattern)
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});
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}
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```
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### Performance Metrics
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- **Latency**: p50, p95, p99 for each algorithm
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- **Throughput**: Sequences processed per second
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- **Memory**: Peak memory usage
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- **Cache efficiency**: Hit rate, miss penalty
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- **Scalability**: Performance vs sequence length
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## Error Handling
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```rust
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#[derive(Debug, Error)]
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pub enum TemporalCompareError {
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#[error("Sequence too long: {0} (max: {1})")]
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SequenceTooLong(usize, usize),
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#[error("Invalid algorithm: {0}")]
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InvalidAlgorithm(String),
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#[error("Cache error: {0}")]
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CacheError(String),
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#[error("Pattern not found")]
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PatternNotFound,
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}
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```
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## Testing Strategy
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### Unit Tests
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- Test DTW with known sequences
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- Verify LCS correctness
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- Test edit distance calculations
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- Validate caching behavior
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### Integration Tests
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- Test with real conversation data
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- Verify pattern detection accuracy
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- Test anomaly detection sensitivity
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- Validate performance requirements
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### Benchmarks
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- Compare against baseline algorithms
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- Measure scaling behavior
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- Test cache effectiveness
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- Profile memory usage
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## Security Considerations
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1. **Input Validation**: Prevent excessively long sequences (DoS)
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2. **Resource Limits**: Cap memory usage and computation time
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3. **Privacy**: Ensure sequence data is not logged in production
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4. **Determinism**: Ensure reproducible results for auditing
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## Monitoring and Observability
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### Metrics to Track
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- Comparison latency distribution
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- Pattern detection accuracy
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- Cache hit/miss ratios
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- Memory usage trends
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- Error rates by type
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### Logging
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```rust
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tracing::info!(
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sequence_length = seq.len(),
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algorithm = ?algorithm,
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latency_ms = latency,
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"Temporal comparison completed"
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);
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```
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## Success Criteria
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- [ ] DTW latency < 10ms for n=100
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- [ ] LCS latency < 5ms for n=100
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- [ ] Pattern detection < 50ms
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- [ ] Cache hit rate > 80%
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- [ ] Zero regressions in existing benchmarks
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- [ ] Full test coverage (>90%)
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- [ ] Documentation complete
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## Future Enhancements
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1. **GPU Acceleration**: Use CUDA for large-scale DTW
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2. **Approximate Algorithms**: Trade accuracy for speed
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3. **Online Learning**: Adapt similarity metrics over time
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4. **Multi-dimensional**: Support vector sequences
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5. **Distributed**: Scale across multiple nodes
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## References
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[1] Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization.
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[2] Bergroth, L., et al. (2000). Survey of longest common subsequence algorithms.
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[3] Levenshtein, V. I. (1966). Binary codes capable of correcting deletions.
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[4] Agrawal, R., & Srikant, R. (1995). Mining sequential patterns.
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[5] Keogh, E., & Ratanamahatana, C. A. (2005). "Exact indexing of dynamic time warping." Knowledge and Information Systems, 7(3), 358-386.
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## Appendix A: Algorithm Complexity
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| Algorithm | Time Complexity | Space Complexity |
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|-----------|----------------|------------------|
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| DTW | O(n²) | O(n²) |
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| LCS | O(nm) | O(nm) |
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| Edit Distance | O(nm) | O(n) |
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| Pattern Search | O(nm) | O(m) |
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## Appendix B: Example Usage
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```rust
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use midstream::temporal_compare::*;
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// Create comparator
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let mut comparator = TemporalComparator::new();
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// Compare sequences
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let similarity = comparator.compare(
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&seq1,
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&seq2,
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ComparisonAlgorithm::DTW,
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);
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// Find similar sequences
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let similar = comparator.find_similar(&query, 0.8);
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// Detect patterns
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let patterns = comparator.detect_pattern(&data, &pattern);
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```
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