Auto-tuned Hadoop MapReduce for ECG analysis

Electrocardiograph (ECG) analysis brings a lot of technical concerns because ECG is one of the tools frequently used in the diagnosis of cardiovascular disease. According to World Health Organization (WHO) statistic in 2012, cardiovascular disease constitutes about 48% of non-communicable deaths worldwide. Although there are many ECG related researches, there is not much efforts in big data computing for ECG analysis which involves dataset more than one gigabyte. ECG files contain graphical data and the size grows as period of data recording gets longer. Big data computing for ECG analysis is critical when many patients are involved. Recently, the implementation of Hadoop MapReduce in cloud computing becomes a new trend due to its parallel computing characteristic which is preferable in big data computing. Since large ECG dataset consume much time in analysis processes, this project will construct a cloud computing approach for ECG analysis using MapReduce in order to investigate the effect of MapReduce in enhancing ECG analysis efficiency in cloud computing. However, the performance of existing MapReduce approach is limited to its configuration based on many factors such as behaviors of cluster and nature of computing processes. Hence, this research proposes MapReduce Auto-Tuning approach using Genetic Algorithm (GA) to enhance MapReduce performance in cloud computing for ECG analysis. The project is expected to reduce ECG analysis process time for large ECG dataset compared to default Hadoop MapReduce.