RoboFlux Whitepaper
  • RoboFlux AI: Comprehensive Technical Whitepaper
  • 1. Introduction
  • 2. System Overview
  • 3. Modular Architecture
  • 4. Data Ingestion and Preprocessing Layer
    • 4.1 Data Stream Typologies
    • 4.2 Data Pipeline Orchestration
    • 4.3 Data Normalization & Feature Engineering
  • 5. Anomaly Detection Subsystem
    • 5.1 Tensor-Based Anomaly Detection
    • 5.2 Online Learning Adaptation
  • 6. Quantum-Inspired Path and Task Optimization
  • 7. Secure Webhook Integration Framework
  • 8. AI Knowledge Hub Implementation
  • 9. Report Generation and Distribution Mechanism
  • 10. Deployment Modalities
  • 11. Cybersecurity and Compliance Protocols
  • 12. Future Roadmap and Extensibility
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  1. 4. Data Ingestion and Preprocessing Layer

4.2 Data Pipeline Orchestration

A distributed data ingestion pipeline, constructed atop high-throughput message queues (e.g., Apache Kafka, RabbitMQ), facilitates reliable, scalable collection and dispatch of incoming data packets to processing microservices.

Key features include:

  • Schema Validation Engines: Enforce JSON/CSV schema conformity, ensuring syntactic and semantic correctness.

  • Time Synchronization Modules: Apply vector clock algorithms to synchronize event timestamps across asynchronous sources.

  • Anomaly Pre-Filters: Execute preliminary statistical outlier rejection based on Z-score and IQR metrics, reducing computational overhead for the anomaly detection core.

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Last updated 12 days ago