2014 ◽  
Vol 631-632 ◽  
pp. 1049-1052
Author(s):  
Sen Pan ◽  
Li Peng Zhu ◽  
Bin Hu ◽  
Pei Yang

According to the rapid growth of the scale of the data at present, the traditional model of data analysis based on stand-alone can't meet to the storage and processing of massive data. With the rise of big data technology, integrating the traditional method of data analysis with big data platform to improve the efficiency of data analysis has become a research direction. This paper analyzes the integration of offline data processing and the Hadoop HDFS, MapReduce, designs a large offline data analysis system platform based on Hadoop, and introduces the core function module.


2018 ◽  
Vol 7 (3.33) ◽  
pp. 248
Author(s):  
Young-Woon Kim ◽  
Hyeopgeon Lee

In the automobile industry, the contract information of vehicles contracted through sales activities, as well as the order data of customers who purchased cars, and vehicle maintenance history information all accumulate in relational databases over time. Although accumulated customer and vehicle information is used for marketing purposes, processing and analyzing this massive data is difficult, as its volume con-stantly increases. This problem of managing big data is commonly solved by utilizing the MapReduce distributed structure of Hadoop, which uses big data distributed processing technology, and R, which is a widely used big data analysis technology. Among the methods that interconnect Hadoop and R, the R and Hadoop integrated programming environment (RHIPE) was developed in this study as a real-time big data analysis system for marketing in the automobile industry. RHIPE allows us to maintain an interactive environment and use the powerful analytical features of R, which is an interpreter language, while achieving a high processing speed using Map and Reduce func-tions. In this study, we developed a real-time big data analysis system that can analyze the orders, reservations, and maintenance history contained in big data using the RHIPE method. 


Author(s):  
Jianqiang Li ◽  
Jia Cui ◽  
Bo Wang ◽  
Qi Wang ◽  
Ge Fu ◽  
...  

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