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The use of Local API(Anomaly Process Instances) Detection for Analyzing Container Terminal Event
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The use of Local API(Anomaly Process Instances) Detection for Analyzing Container Terminal Event
Jeon, Daeuk; Bae, Hyerim;
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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.
Anomaly;Process Mining;Process Instance;Local Anomaly;Container Terminal;
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인공신경망을 이용한 항만터미널에서 컨테이너의 비정상 이송 프로세스 예측,전대욱;배혜림;

한국SCM학회지, 2015. vol.15. 2, pp.117-126
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