Kwasa-Kwasa Domain Expansion Implementation Plan

This document outlines the detailed implementation steps for expanding Kwasa-Kwasa beyond text processing to handle genomic data and pattern-based meaning extraction.

Phase 1: Core Framework Abstraction (Weeks 1-3)

Week 1: Unit Boundary Generalization

Week 2: Unit Operation Generalization

Week 3: Plugin System Architecture

Phase 2: Genomic Analysis Extension (Weeks 4-7)

Week 4: Genomic Unit Types

Week 5: Genomic Boundary Detection

Week 6: Genomic Operations Library

Week 7: Genomic Pipeline Components

Phase 3: Pattern-Based Meaning Extraction (Weeks 8-10)

Week 8: Statistical Analysis Components

Week 9: Pattern Recognition Algorithms

Week 10: Meaning Extraction Components

Phase 4: Integration and Validation (Weeks 11-12)

Week 11: Turbulance Language Integration

Week 12: Testing and Documentation

Implementation Details

Core Abstraction API (Draft)

/// Generic trait for any unit of analysis
pub trait Unit: Clone + Debug {
    /// The raw content of this unit
    fn content(&self) -> &[u8];
    
    /// Human-readable representation
    fn display(&self) -> String;
    
    /// Metadata associated with this unit
    fn metadata(&self) -> &Metadata;
    
    /// Unique identifier for this unit
    fn id(&self) -> UnitId;
}

/// Generic trait for boundary detection in any domain
pub trait BoundaryDetector {
    type UnitType: Unit;
    
    /// Detect boundaries in the given content
    fn detect_boundaries(&self, content: &[u8]) -> Vec<Self::UnitType>;
    
    /// Configuration for the detection algorithm
    fn configuration(&self) -> &BoundaryConfig;
}

/// Generic operations on units
pub trait UnitOperations<T: Unit> {
    /// Split a unit into smaller units based on a pattern
    fn divide(&self, unit: &T, pattern: &str) -> Vec<T>;
    
    /// Combine two units with appropriate transitions
    fn multiply(&self, left: &T, right: &T) -> T;
    
    /// Concatenate units with intelligent joining
    fn add(&self, left: &T, right: &T) -> T;
    
    /// Remove elements from a unit
    fn subtract(&self, source: &T, to_remove: &T) -> T;
}

Genomic Extension API (Draft)

/// Represents a DNA/RNA sequence unit
pub struct NucleotideSequence {
    content: Vec<u8>,
    metadata: Metadata,
    id: UnitId,
}

impl Unit for NucleotideSequence {
    // Implementation of the Unit trait
}

/// Detects boundaries in genomic sequences
pub struct GenomicBoundaryDetector {
    config: BoundaryConfig,
}

impl BoundaryDetector for GenomicBoundaryDetector {
    type UnitType = NucleotideSequence;
    
    fn detect_boundaries(&self, content: &[u8]) -> Vec<NucleotideSequence> {
        // Implementation for genomic boundary detection
    }
    
    fn configuration(&self) -> &BoundaryConfig {
        &self.config
    }
}

/// Operations specific to genomic sequences
pub struct GenomicOperations;

impl UnitOperations<NucleotideSequence> for GenomicOperations {
    // Implementation of standard operations for genomic sequences
}

// Extension methods for genomic analysis
impl NucleotideSequence {
    /// Translate DNA to protein
    pub fn translate(&self) -> ProteinSequence {
        // Implementation
    }
    
    /// Find open reading frames
    pub fn find_orfs(&self) -> Vec<NucleotideSequence> {
        // Implementation
    }
    
    /// Align with another sequence
    pub fn align_with(&self, other: &NucleotideSequence) -> Alignment {
        // Implementation
    }
}

Pattern Analysis API (Draft)

/// Analyzes character patterns in any unit type
pub struct PatternAnalyzer<T: Unit> {
    config: PatternConfig,
    _unit_type: PhantomData<T>,
}

impl<T: Unit> PatternAnalyzer<T> {
    /// Calculate frequency distribution of elements
    pub fn frequency_distribution(&self, unit: &T) -> HashMap<Vec<u8>, f64> {
        // Implementation
    }
    
    /// Calculate Shannon entropy
    pub fn shannon_entropy(&self, unit: &T) -> f64 {
        // Implementation
    }
    
    /// Detect statistically significant patterns
    pub fn significant_patterns(&self, unit: &T) -> Vec<Pattern> {
        // Implementation
    }
    
    /// Compare against expected distribution
    pub fn deviation_from_expected(&self, unit: &T, expected: &Distribution) -> DeviationScore {
        // Implementation
    }
}

/// Orthographic analysis for text units
pub struct OrthographicAnalyzer {
    config: OrthographicConfig,
}

impl OrthographicAnalyzer {
    /// Analyze visual density of text
    pub fn visual_density(&self, text: &TextUnit) -> DensityMap {
        // Implementation
    }
    
    /// Extract root patterns based on etymology
    pub fn etymological_roots(&self, text: &TextUnit) -> Vec<RootPattern> {
        // Implementation
    }
}

Resource Requirements

Risk Assessment

Risk Impact Likelihood Mitigation
Genomic operations performance issues High Medium Optimize critical algorithms, use parallelization
Generalization breaks existing text functionality High Low Comprehensive test suite, backward compatibility tests
Domain-specific complexity overwhelms the framework Medium Medium Clear abstraction boundaries, focused scope for initial implementation
Integration difficulties with existing bioinformatics tools Medium High Adopt standard file formats, provide conversion utilities
Pattern analysis yields limited meaningful results Low Medium Start with proven statistical approaches, iterative refinement

Success Criteria

The domain expansion will be considered successful when:

  1. The framework can process genomic sequences with the same flexibility as text
  2. Common genomic analysis workflows can be expressed in Turbulance syntax
  3. Pattern analysis yields statistically significant insights
  4. Performance is comparable to specialized tools for common operations
  5. Documentation and examples make the expanded capabilities accessible to users