🏃‍♂️ UltraTrack: Real-Time Telemetry & Analytics Backend

A robust, decoupled backend system designed for the real-time monitoring, management, and analysis of runners in ultra-distance endurance events (e.g., 50km, 82km, and 100-mile races).

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🎯 Project Overview

UltraTrack is built to process massive volumes of telemetry data originating from athletes' GPS watches and heart rate monitors.

🚀 Core Objectives

• Real-Time Processing: Parse streams of telemetry data accurately.
• Predictive Analytics: Calculate Estimated Time of Arrival (ETA) and predict physical depletion (e.g., hydration, sodium loss) based on current pacing and terrain.
• Health Anomaly Detection: Dynamically generate and route medical or pacing alerts.
• Decoupled Distribution: Distribute processed insights to various consumer interfaces (organizer dashboards, SMS gateways, logging systems) without blocking the main processing thread.

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🏛️ Architectural Design

The project strictly adheres to Clean Architecture principles, decoupling the core business logic from infrastructure details and delivery mechanisms.

graph TD
    subgraph "🟦 Infrastructure Layer (External)"
        I[🔄 Iterator: TelemetryCollection]
        P[🛡️ Proxy: CachedHistoricalAnalyzer]
    end

    subgraph "🟩 Application Layer (Orchestration)"
        F[🚪 Facade: RaceMonitorFacade]
        O[📡 Observer: TelemetrySubject]
    end

    subgraph "🟨 Domain Layer (Core Business)"
        S[🧠 Strategy: PacingStrategy]
        D[🎁 Decorator: AlertDecorator]
        M[(Model: TelemetryData)]
    end

    I --> F
    P --> F
    F --> S
    F --> D
    F --> M
    F --> O

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🧩 GoF Design Patterns Composition

To maintain low coupling, high cohesion, and scalable code, UltraTrack employs a composition of six Gang of Four (GoF) design patterns.

Pattern	Type	Location	Role
🚪 Facade	Structural	application.facade	Provides a single, unified entry point (processNewTelemetry()) to hide complex subsystem orchestration from external clients.
🔄 Iterator	Behavioral	infrastructure.iterator	Standardizes the traversal of complex incoming telemetry data streams (JSON, queues) without exposing internal representations.
🛡️ Proxy	Structural	infrastructure.proxy	Intercepts resource-intensive historical data queries, utilizing caching and lazy initialization to protect server memory.
🧠 Strategy	Behavioral	domain.strategy	Injects distinct mathematical strategies (e.g., FlatTerrainStrategy, MountainTrailStrategy) at runtime based on geolocation, avoiding massive conditional blocks.
🎁 Decorator	Structural	domain.decorator	Dynamically wraps basic health alerts with additional behaviors (e.g., HighPriority, Encrypted) at runtime to prevent subclass explosion.
📡 Observer	Behavioral	application.observer	Uses a Pub/Sub model to notify decoupled endpoints (dashboards, SMS) whenever the core engine processes new telemetry.

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📂 Directory Structure

src/main/java/com/ultratrack/
├── domain/                     
│   ├── model/                  
│   ├── strategy/               
│   └── decorator/              
├── application/                
│   ├── facade/                 
│   └── observer/               
└── infrastructure/             
    ├── proxy/                  
    └── iterator/               

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🛠️ Local Development & Setup

The project is optimized for automated infrastructure and runs smoothly in Unix-based environments.

1. Clone the repository via Git.
2. Environment Setup: For the most native and frictionless build experience, especially when dealing with containerization later on, we highly recommend using WSL2 (Ubuntu) if you are developing on a Windows machine.
3. Access Control: Ensure your local SSH keys are configured for secure repository interactions.
4. Build: Run the standard Java build sequence to resolve dependencies.

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📅 Next Steps for Development

• Define the internal fields of the domain.model.TelemetryData entity.
• Implement the concrete classes for the Strategy pattern to handle math/physics calculations.
• Wire the Observers to mock terminal outputs for local testing.