🤖 AI Summary
In industrial and IoT applications, high-ratio compression of real-time/historical process data reduces storage costs and improves efficiency but risks degrading the accuracy of statistical analysis, anomaly detection, and machine learning models. This paper systematically evaluates the impact of mainstream time-series compression algorithms—including PCA, SAX, and Delta encoding—on the preservation of critical data features, integrating theoretical analysis, controlled simulation experiments, and multi-scenario empirical validation. We first quantify the nonlinear relationship between compression ratio and analytical bias, identifying safety thresholds that guarantee analysis fidelity. Building on these findings, we propose a hierarchical compression strategy and engineering best practices that jointly optimize storage efficiency and analytical reliability. Results demonstrate that moderate compression preserves over 95% of model performance, whereas compression beyond the identified thresholds severely distorts statistical metrics and causes sharp declines in prediction accuracy.
📝 Abstract
In industrial and IoT environments, massive amounts of real-time and historical process data are continuously generated and archived. With sensors and devices capturing every operational detail, the volume of time-series data has become a critical challenge for storage and processing systems. Efficient data management is essential to ensure scalability, cost-effectiveness, and timely analytics. To minimize storage expenses and optimize performance, data compression algorithms are frequently utilized in data historians and acquisition systems. However, compression comes with trade-offs that may compromise the accuracy and reliability of engineering analytics that depend on this compressed data. Understanding these trade-offs is essential for developing data strategies that support both operational efficiency and accurate, reliable analytics. This paper assesses the relation of common compression mechanisms used in real-time and historical data systems and the accuracy of analytical solutions, including statistical analysis, anomaly detection, and machine learning models. Through theoretical analysis, simulated signal compression, and empirical assessment, we illustrate that excessive compression can lose critical patterns, skew statistical measures, and diminish predictive accuracy. The study suggests optimum methods and best practices for striking a compromise between analytical integrity and compression efficiency.