Real Time Data and Decisions in Aarhus and Surrey Smart

This study investigates the performance of real-time data processing systems in smart cities, with a focus on the implementation of autonomous decision-making processes for urban management. The research collects data sets from Aarhus (Denmark) and Surrey (Canada) to analyze data processing performance using traffic, parking, pollution and water consumption datasets. The data processing timeframe and data passage rates benefit from implementing the min-max normalization technique together with other filtration methods. The BDA-based smart city architecture shows a positive effect on real-time decision efficiency which benefits traffic congestion control and parking space tracking and water consumption assessment. Through independent event-driven notifications the system helps urban management as well as improves service quality delivered to city dwellers. Application of data filtration improves Hadoop and Spark-based framework performance for large dataset processing according to the study which demonstrates better processing time and throughput results.