• International Commerce and Information Review
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• v.15 no.1
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• pp.357-379
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• 2013

Recently, the government was introduced as currency for earning of raw materials purchasing system for electronic Government management of foreign trade in 2011, and revised regulations for electronic proof of purchase. In addition, the currency for earning of raw materials, such as procurement system in 2012, followed by electronic proof of purchase local letter of credit. The government electronic trading will be promoting local trading of electronic procedures. This study will be a preceding research on the goods control system for acquiring foreign currency and the obligation of using digitalized approval of purchase due to the revision of foreign trade law. Also, it will conduct theoretical and legal research regarding the obligation of digital establishment of the local L/C which is a result of amendments to the rules of operation for the Bank of Korea's trade finance. Further, it will analyze the legal and operational problems and its response plans for the establishment of the local trade integrated management system which promotes the digitalization of the local trade process.

• The Journal of Society for e-Business Studies
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• v.20 no.4
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• pp.41-59
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• 2015

Information systems has been developed and used in various business area, therefore there are abundance of history data (log data) stored, and subsequently, it is required to analyze those log data. Previous studies have been focusing on the discovering of relationship between events and no identification of anomaly instances. Previously, anomaly instances are treated as noise and simply ignored. However, this kind of anomaly instances can occur repeatedly. Hence, a new methodology to detect the anomaly instances is needed. In this paper, we propose a methodology of LAPID (Local Anomaly Process Instance Detection) for discriminating an anomalous process instance from the log data. We specified a distance metric from the activity relation matrix of each instance, and use it to detect API (Anomaly Process Instance). For verifying the suggested methodology, we discovered characteristics of exceptional situations from log data. To demonstrate our proposed methodology, we performed our experiment on real data from a domestic port terminal.