Empirical Rapid and Accurate Prediction Model for Data Mining Tasks in CloudComputing Environments

With the arrival of big data and cloud computing asa computing concept, it is becoming ever more critical toefficiently choose the most optimum machine on which toexecute a program, for example in the healthcare environment.This process of choice is also complicated by the fact thatnumerous machines are available as virtual machines. Hence,predicting the most optimum choice of machine based on atarget application is a challenge. Prediction techniques consumelarge amount of computing resources when operating withmulti-dimensional data that can cause long delays compoundedby cross validation process in evaluating and choosing the mostoptimum prediction model. We propose a model of predictiontechniques to predict and classify some of the health datasets toretrieve useful knowledge to illustrate how a data miner canchoose a suitable machine especially in cloud environment withgood accuracy in a timely manner. Our results show that theexecution time has an inverse relation with the use of resourcesof a machine and the accuracy of prediction could be differentfrom one machine to another using the same predictingtechnique and dataset.

Empirical Rapid and Accurate Prediction Model for Data Mining Tasks in CloudComputing Environments Keywords:

Computer architectures , Data Miner , Predicting techniques , Cloud computing , and Healthcare Datasets