Data Module - Abstract Documentation

Purpose and Responsibility

The data module serves as the central hub for all data collection, processing, and export functionality in the research system. It orchestrates multiple data streams including audio recordings, user interactions, sensor data, performance metrics, and comprehensive data export capabilities.

Key Data Structures and Relationships

Module Organization

  • audio_recording: Think-aloud protocol recording and analysis
  • export: Comprehensive data export and statistical analysis preparation
  • interaction_tracking: Detailed user interaction pattern capturing
  • performance_tracing: System performance monitoring and optimization
  • sensor_integration: External physiological sensor data collection

Core Data Flow

  1. Collection Layer: Multiple concurrent data streams (audio, interactions, sensors, performance)
  2. Processing Layer: Real-time analysis, quality assessment, and pattern detection
  3. Export Layer: Structured data transformation for analysis tools (R, Python, SPSS)

Main Data Flows and Transformations

Input Streams

  • Audio data from microphone systems across platforms
  • Mouse/keyboard interaction events with timing precision
  • External sensor readings (EEG, GSR, eye-tracking)
  • System performance metrics and timing data

Processing Pipeline

  1. Real-time Collection: Concurrent data streams with timestamp synchronization
  2. Quality Assessment: Signal quality, data completeness, error detection
  3. Pattern Analysis: Behavioral pattern detection, cognitive load estimation
  4. Data Validation: Integrity checks, outlier detection, missing data handling

Output Formats

  • JSON for structured data interchange
  • CSV for statistical analysis software
  • Platform-specific analysis scripts (R, Python, SPSS)
  • Real-time metrics for live monitoring

External Dependencies and Interfaces

Platform Integration

  • Audio Systems: Core Audio (macOS), AVAudioRecorder (iOS), MediaRecorder (Android)
  • Sensor APIs: Device-specific SDKs for EEG, GSR, eye-tracking equipment
  • Performance Monitoring: System tracing APIs and performance counters

Analysis Tool Compatibility

  • R Integration: Mixed-effects models, learning curve analysis, group comparisons
  • Python Integration: Scientific computing stack (pandas, numpy, scipy)
  • SPSS Integration: Syntax generation for statistical procedures

State Management Patterns

Session-Based Architecture

  • Session Lifecycle: Start → Active Recording → Quality Assessment → Export
  • Multi-Stream Coordination: Synchronized timestamps across all data sources
  • Buffer Management: Circular buffers for real-time data with configurable retention

Data Integrity

  • Atomic Operations: Complete session recording or rollback
  • Error Recovery: Graceful handling of sensor failures or data corruption
  • Privacy Controls: Configurable data retention and anonymization policies

Core Algorithms and Business Logic Abstractions

Audio Analysis

  • Think-Aloud Classification: NLP-based categorization of cognitive processes
  • Speech Quality Assessment: Signal-to-noise ratio, clipping detection, silence analysis
  • Real-time Transcription: Automated speech recognition with confidence scoring

Interaction Pattern Analysis

  • Keystroke Dynamics: Inter-key intervals, dwell times, correction patterns
  • Mouse Movement Profiling: Velocity, acceleration, trajectory smoothness
  • Hesitation Detection: Pause analysis, behavioral uncertainty markers

Sensor Data Processing

  • Multi-Modal Synchronization: Timestamp alignment across sensor modalities
  • Artifact Detection: Automated identification of sensor noise and movement artifacts
  • Feature Extraction: Time-domain and frequency-domain signal characteristics

Export and Analysis Preparation

  • Population Analytics: Cross-participant comparisons and group statistics
  • Learning Curve Generation: Progress tracking and performance trajectory analysis
  • Statistical Modeling: Data formatting for mixed-effects models and hypothesis testing

Performance Considerations

  • Concurrent Processing: Multi-threaded data collection with minimal interference
  • Memory Management: Efficient buffering strategies for long recording sessions
  • Disk I/O Optimization: Asynchronous writing and compression for large datasets
  • Real-time Constraints: Sub-millisecond timing accuracy for time-sensitive measurements

Security and Privacy Implications

  • Data Anonymization: Configurable PII removal and participant ID obfuscation
  • Consent Management: Fine-grained control over data collection and retention
  • Secure Storage: Encrypted data at rest with configurable retention policies
  • Access Controls: Role-based permissions for data export and analysis