Distributed Based Serial Regression Multiple Imputation for High Dimensional Multivariate Data in Multicore Environment of Cloud

Abstract

Multiple imputations (MI) are predominantly applied in such processes that are involved in the transaction of huge chunks of missing data. Multivariate data that follow traditional statistical models undergoes great suffering for the inadequate availability of pertinent data. The field of distributed computing research faces the biggest hurdle in the form of insufficient high dimensional multivariate data. It mainly deals with the analysis of parallel input problems found in the cloud computing network in general and evaluation of high-performance computing in particular. In fact, it is a tough task to utilize parallel multiple input methods for accomplishing remarkable performance as well as allowing huge datasets achieves scale. In this regard, it is essential that a credible data system is developed and a decomposition strategy is used to partition workload in the entire process for minimum data dependence. Subsequently, a moderate synchronization and/or meager communication liability is followed for placing parallel impute methods for achieving scale as well as more processes. The present article proposes many novel applications for better efficiency. As the first step, this article suggests distributed-oriented serial regression multiple imputation for enhancing the efficiency of imputation task in high dimensional multivariate normal data. As the next step, the processes done in three diverse with parallel back ends viz. Multiple imputation that used the socket method to serve serial regression and the Fork Method to distribute work over workers, and also same work experiments in dynamic structure with a load balance mechanism. In the end, the set of distributed MI methods are used to experimentally analyze amplitude of imputation scores spanning across three probable scenarios in the range of 1:500. Further, the study makes an important observation that due to the efficiency of numerous imputation methods, the data is arranged proportionately in a missing range of 10% to 50%, low to high, while dealing with data between 1000 and 100,000 samples. The experiments are done in a cloud environment and demonstrate that it is possible to generate a decent speed by lessening the repetitive communication between processors.