Description

The surge of medical and e-commerce applicationshas generated tremendous amount of data, which brings people toa so-called “Big Data” era. Different from traditional large datasets, the term “Big Data” not only means the large size of datavolume but also indicates the high velocity of data generation.However, current data mining and analytical techniques arefacing the challenge of dealing with large volume data in a shortperiod of time. This paper explores the efficiency of utilizing theNormal Distribution (ND) method for splitting and processinglarge volume medical data in cloud environment, which canprovide representative information in the split data sets. TheND-based new model consists of two stages. The first stage adoptsthe NDmethod for large data sets splitting and processing, whichcan reduce the volume of data sets. The second stage implementsthe ND-based model in a cloud computing infrastructure forallocating the split data sets. The experimental results showsubstantial efficiency gains of the proposed method over theconventional methods without splitting data into small partitions.The ND-based method can generate representative data sets,which can offer efficient solution for large data processing. Thesplit data sets can be processed in parallel in Cloud computingenvironment.