• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.515-524
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• 2022

To effectively collect, manage, and share the maritime traffic information, it is necessary to identify the technology trends concerning this particular information and analyze its current status and problems. Therefore, this study observes the domestic and foreign technology trends involving maritime traffic information while analyzing and summarizing the current status and problems in collecting, managing, and sharing it. According to the data analysis, the problems in the collecting stage are difficulties in collecting visual information from long-distance radars, CCTVs, and cameras in areas outside the LTE network coverage. Notably, this explains the challenges in detecting smuggling ships entering the territorial waters through the exclusive economic zone (EEZ) in the early stage. The problems in the management stage include difficult reductions and expansions of maritime traffic information caused by the lack of flexibility in storage spaces mostly constructed by the maritime transportation system. Additionally, it is challenging to deal with system failure with system redundancy and backup as a countermeasure. Furthermore, the problems in the sharing stage show that it is difficult to share information with external operating organizations since the internal network is mainly used to share maritime transportation information. If at all through the government cloud via platforms such as LRIT and SASS, it often fails to effectively provide various S/W applications that help use maritime big data. Therefore, it is suggested that collecting equipment such as unmanned aerial vehicles and satellites should be constructed to expand collecting areas in the collecting stage. In the management and sharing stages, the introduction and construction of private clouds are suggested, considering the operational administration and information disclosure of each maritime transportation system. Through these efforts, an enhancement of the expertise and security of clouds is expected.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.4
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• pp.31-41
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• 2012

The law enforcement agencies in the worldwide are confiscating or retaining computer systems involved in a crime/civil case, if there are any, at the preliminary investigation stage, even though the case does not involve a cyber-crime. They are collecting digital evidences from the suspects's systems and using them in the essential investigation procedure. It requires much time, though, to collect, duplicate and analyze disk images in general crime cases, especially in cases in which rapid response must be taken such as kidnapping and murder cases. The enterprise forensics, moreover, it is impossible to acquire and duplicate hard disk drives in mass storage server, database server and cloud environments. Therefore, it is efficient and effective to selectively collect only traces of the behavior of the user activities on operating systems or particular files in focus of triage investigation. On the other hand, if we acquire essential digital evidences from target computer, it is not forensically sound to collect just files. We need to use standard digital evidence container from various sources to prove integrity and probative of evidence. In this article, we describe a new digital evidence container, we called Xebeg, which is easily able to preserve collected digital evidences selectively for using general technology such as XML and PKZIP compression technology, which is satisfied with generality, integrity, unification, scalability and security.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.10
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• pp.1277-1286
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• 2018

In this paper, we provide flexible scalability of computing resources in cloud environment and Apache Hadoop based cloud environment for analysis of public medical information big data. In fact, it includes the ability to quickly and flexibly extend storage, memory, and other resources in a situation where log data accumulates or grows over time. In addition, when real-time analysis of accumulated unstructured log data is required, the system adopts Hadoop-based analysis module to overcome the processing limit of existing analysis tools. Therefore, it provides a function to perform parallel distributed processing of a large amount of log data quickly and reliably. Perform frequency analysis and chi-square test for big data analysis. In addition, multivariate logistic regression analysis of significance level 0.05 and multivariate logistic regression analysis of meaningful variables (p<0.05) were performed. Multivariate logistic regression analysis was performed for each model 3.