Multi-Stage Measurement Data Analysis with Event Stream Processing

The analysis system is divided into multiple independent stages: data collection stage (receiving raw measurement data), preprocessing stage (filtering and validation), analysis stage (applying machine learning models), and output stage (generating loss detection results). Each stage processes data independently and passes results to the next stage, allowing the system to maintain high accuracy while managing complexity through modular architecture.

Machine learning models serve as intermediary components between raw measurement data and loss detection results. These models process and interpret complex measurement patterns, acting as a bridge that transforms raw data into actionable insights without requiring the entire system to handle all computational complexity simultaneously.

The system performs preliminary data validation and filtering before applying machine learning algorithms. Basic anomaly detection rules and threshold-based validation are applied first to eliminate obviously invalid data points, reducing the volume of data that requires computationally intensive machine learning processing and thereby reducing overall processing time.

The machine learning models are applied selectively to data points that pass preliminary validation but exhibit suspicious patterns, rather than processing every single data point through the full machine learning pipeline. This partial application of complex analysis maintains high detection capability for suspicious cases while reducing unnecessary computational overhead for normal data.