Empirical rapid and accurate prediction model for data mining tasks in cloud computing environments

With the arrival of big data and cloud computing as a computing concept, it is becoming ever more critical to efficiently choose the most optimum machine on which to execute a program, for example in the healthcare environment. This process of choice is also complicated by the fact that numerous machines are available as virtual machines. Hence, predicting the most optimum choice of machine based on a target application is a challenge. Prediction techniques consume large amount of computing resources when operating with multi-dimensional data that can cause long delays compounded by cross validation process in evaluating and choosing the most optimum prediction model. We propose a model of prediction techniques to predict and classify some of the health datasets to retrieve useful knowledge to illustrate how a data miner can choose a suitable machine especially in cloud environment with good accuracy in a timely manner. Our results show that the execution time has an inverse relation with the use of resources of a machine and the accuracy of prediction could be different from one machine to another using the same predicting technique and dataset.