Siwoon Son, Dasol Kim, Sujeong Lee, Myeong-Seon Gil, Yang-Sae Moon

KIPS Transactions on Software and Data Engineering, Vol. 6, No. 6, pp. 309-314, Jun. 2017
10.3745/KTSDE.2017.6.6.309,   PDF Download:
Keywords: Dynamic Visualization, Big Data, Real-time Processing, SNS Analysis, Tag Cloud
Abstract

In general, there are many difficulties in collecting, storing, and analyzing SNS (social network service) data, since those data have big data characteristics, which occurs very fast with the mixture form of structured and unstructured data. In this paper, we propose a new data visualization framework that works on Apache Storm, and it can be useful for real-time and dynamic analysis of SNS data. Apache Storm is a representative big data software platform that processes and analyzes real-time streaming data in the distributed environment. Using Storm, in this paper we collect and aggregate the real-time Twitter data and dynamically visualize the aggregated results through the tag cloud. In addition to Storm-based collection and aggregation functionalities, we also design and implement a Web interface that a user gives his/her interesting keywords and confirms the visualization result of tag cloud related to the given keywords. We finally empirically show that this study makes users be able to intuitively figure out the change of the interested subject on SNS data and the visualized results be applied to many other services such as thematic trend analysis, product recommendation, and customer needs identification.

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