An Innovative Model for Extracting OLAP Cubes from NOSQL Database Based on Scalable Naïve Bayes Classifier

Due to unstructured and large amounts of data, relational databases are no longer suitable for data management. As a result, new known as NOSQL have been introduced. The issue is that such database difficult analyze. Online analytical processing (OLAP) the foundational technology analysis in business intelligence. Because these technologies were designed primarily systems, performing OLAP difficult. We present model extracting cubes from document-oriented this article. A scalable Naïve Bayes classifier method was used purpose. proposed solution divided into three stages preparation, Bayes, NBMR. Our algorithm, NBMR, based on (NBC) MapReduce (MR) programming model. Each document with nearly same attribute will belong class, can be perform analysis. allows distributed parallel Classifier computing, it appropriate large-scale sets. proper efficient approach when considering speed reduced number required comparisons.