Optimized hadoop map reduce system for strong analytics of cloud big product data on amazon web service

Because of the rapid increase of data in the cloud of Amazon Web Service (AWS), the traditional methods for analyzing this data are not good and inappropriate, so unconventional methods of analysis have been proposed by many data scientists such as concurrent/ parallel techniques to meeting the requirements of performance and scalability entailed in such big data analyses. In this paper we are used Hadoop Map Reduce system that contains Hadoop Distributed File System (HDFS) and Hadoop cluster. We optimized it by combining it with five efficient Data Mining (DM) algorithms such as Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Correlative Naive Bayes classifier (CNB), and Fuzzy CNB (FCNB) for strong analytics of cloud big data. The proposed system applied on product review data that taken form the cloud of AWS. The Evaluation of Hadoop Map Reduce done with important benchmarks as Mean Absolute Percentage Error (MPAE), Root Mean Square Error (RMSE), and runtime for word count, sort, inverted index. Also, the evaluation of DM models with Hadoop Map Reduce system done by using accuracy, sensitivity, specificity, memory, and running time. Experiments have shown that FCNB is effective in addressing the problem of big data.

Automated summary

Due to the rapid increase of data in AWS cloud, traditional methods of data analysis are not suitable. Non-traditional methods, such as concurrent/parallel techniques, have been proposed by data scientists to meet the performance and scalability requirements of big data analyses. This paper utilizes the Hadoop Map Reduce system, combined with five efficient data mining algorithms, to perform strong analytics on cloud big data. The proposed system is applied to product review data from AWS cloud, and is evaluated using important benchmarks and metrics. The experiments show that FCNB is effective in addressing the problem of big data.