Optimized hadoop map reduce system for strong analytics of cloud big product data on amazon web service |
| |
| Institution: | 1. Zhejiang University of Science and Technology, Hangzhou 310023, China;2. School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, China;3. Zhejiang Dingli Industrial Company, Lishui 321400, China;4. College of Engineering & IT, University of Dubai, UAE;1. School of Cyber Science and Engineering, Sichuan University, Chengdu, China;2. Cybersecurity Research Institute, Sichuan University, Chengdu, China;1. Key Laboratory of Grain Information Processing and Control (Henan University of Technology), Ministry of Education, Zhengzhou 450001, PR China.;2. College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, PR China;3. School of Control Science and Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, PR China;4. School of Science, Dalian Maritime University, Dalian 116026, PR China;5. Key Laboratory of Education Informatization for Nationalities, Ministry of Education, Yunnan Normal University, Kunming 650500, PR China |
| |
| Abstract: | 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 Naïve 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. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
