Abstract—Recently, various network analysis methods has been utilized to reveal undisclosed knowledge in a variety of fields. In particular, these methods are used to reveal potential trade risk factors in e-Customs. However, existing methods do not provide a fast response time to user queries, mainly due to the large size of the data and the complexity of relationships between the data in e-Custom. In this paper, we propose an efficient network analysis system for revealing potential trade risk factors in e-Custom. The system proposes an efficient subgraph matching method and visualization tool to find the relationships between the data in a network. It quickly finds complicated relationships and dramatically reduces the number of unnecessary searches. Also, to verify the superiority of our method, we compare our method with existing method in various experiments.
Index Terms—Network analysis, subgraph matching, e-customs, big-data.
Dongmin Seo and Min-Ho Lee are with the Korea Institute of Science and Technology Information, Daejeon, Republic of Korea (e-mail: dmseo@ kisti.re.kr, cokeman@ kisti.re.kr).
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Cite: Dongmin Seo and Min-Ho Lee, "Development of Efficient Network Analysis System for Revealing Potential Trade Risk Factors in e-Customs," International Journal of Future Computer and Communication vol. 7, no. 1, pp. 6-9, 2018.
