Parallel computing based iterative approach for the substantial weather forecasting

The need of computational environment and processing power increases per day due to the large amount of data generating real-time applications like e-Healthcare systems, manufacturing systems, e-Government sites, online shopping portals, social networking sites, and weather and agricultural forecasting applications. For the purpose of handling large data, and to find insights from such data, a platform with methodologies is mandatory. In this paper, we proposed a parallel computing based iterative approach to give the analytics and improve the performance of the system. We tested the approach on the historical data of weather published by Open Government Data Platform of India. The proposed system based approach are used to handle a large amount of data and capable of processing it on the parallel computing platform. The approach is used to forecast the results by applying different parameters present in the database. The main aim of the work is to reduce the execution time required and forecast the results. In this study, the experiments are tested on parallel computing workers and gives multiple of 100 times better performance than the single worker system.