Data Governance Frameworks for Large-Scale Data Processing and Optimization

Abstract

The rapid expansion of smart city ecosystems has resulted in the generation of large-scale, heterogeneous traffic data, requiring effective data governance and optimized processing frameworks. However, existing cloud-based methods often lack robust governance mechanisms, leading to issues such as poor data quality, high latency, and unreliable traffic prediction. This research addresses these limitations by proposing a data governance framework integrated with an Artificial Butterfly Optimized Back Propagation Neural Network (ABO-BPNN) for traffic congestion prediction. The proposed framework incorporates data standardization, quality assurance, metadata management, and secure data access within a scalable architecture to ensure efficient large-scale data handling. The framework is trained on a traffic dataset collected from IoT sensors, including vehicle speed, traffic density, weather conditions, and incident-related information. Data preprocessing is performed using Min-Max normalization to remove noisy data. Feature extraction is performed with Principal Component Analysis (PCA) to reduce dimensionality and enhance significant traffic patterns. The ABO algorithm optimizes the weights of the BPNN, therebyimproving convergence and prediction capability. The framework is implemented using Python with TensorFlow, and experimental results demonstrate enhanced prediction 96.21% accuracy, 95.30% precision, 94.78% recall, and a 94.95% F1-score. The research concludes that integrating data governance with optimization-driven neural networks significantly improves large-scale traffic data processing in smart city environments.