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http://dx.doi.org/10.36498/kbigdt.2020.5.1.147

Partition-based Big Data Analysis and Visualization Algorithm  

Hong, Jun-Ki (배재대학교 컴퓨터공학과)
Publication Information
The Journal of Bigdata / v.5, no.1, 2020 , pp. 147-154 More about this Journal
Abstract
Today, research is actively being conducted to derive meaningful results from big data. In this paper, we propose a partition-based big data analysis algorithm that can analyze the correlation between variables by setting the data areas of big data as partitions and calculating the representative values of each partition. In this paper, the analyzed visualization results are compared according to the partition size of a proposed partition-based big data analysis (PBDA) algorithm that can control the size of the partition. In order to verify the proposed PBDA algorithm, the big data of 'A' is analyzed, and meaningful results are obtained through the analysis of changes in sales volume of products according to changes in temperature and sales price.
Keywords
Big Data; Analytics; Visualization; Partition;
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