• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1397-1404
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• 2018

Due to the technical difficulties and high cost for obtaining 3-dimensional structure data, sequence-based approaches in proteins have not been widely acknowledged. A motif can be defined as any segments in protein or gene sequences. With this simplicity, motifs have been actively and widely used in various areas. However, the motif itself has not been studied comprehensively. The value of this study can be categorized in three fields in order to analyze the human proteins using artificial intelligence method: (1) Based on our best knowledge, this research is the first comprehensive motif analysis by analyzing motifs with all human proteins in Protein Data Bank (PDB) associated with the database of Enzyme Commission (EC) number and Structural Classification of Proteins (SCOP). (2) We deeply analyze the motif in three different categories: pattern, statistical, and functional analysis of clusters. (3) At the last and most importantly, we proposed random motif frequency(RMF) matric that can efficiently distinct the characteristics of proteins by identifying interface residues from non-interface residues and clustering protein functions based on big data while varying the size of random motif.

• Database Research
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• v.34 no.3
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• pp.58-68
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• 2018

Distributed computing helps to efficiently store and process large data on a cluster of multiple machines. The performance of distributed computing is greatly influenced depending on the state of the servers constituting the distributed system. In this paper, we propose a self-diagnosis system that collects log data in a distributed system, detects anomalies and visualizes the results in real time. First, we divide the self-diagnosis process into five stages: collecting, delivering, analyzing, storing, and visualizing stages. Next, we design a real-time self-diagnosis system that meets the goals of real-time, scalability, and high availability. The proposed system is based on Apache Flume, Apache Kafka, and Apache Storm, which are representative real-time distributed techniques. In addition, we use simple but effective moving average and 3-sigma based anomaly detection technique to minimize the delay of log data processing during the self-diagnosis process. Through the results of this paper, we can construct a distributed real-time self-diagnosis solution that can diagnose server status in real time in a complicated distributed system.

• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.129-137
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• 2019

In this paper, we propose the purpose of this study is to examine whether the library's creation shared value activities in China and Korea affect brand quality, social status, and the influence of each variable according to the Chinese and Korean groups. To achieve the purpose of this study, the survey was conducted using questionnaires to users who have used the Big Data Sharing Service in Korean and Chinese libraries. A total of 500 questionnaires were distributed to participants in the study, and 460 of the recovered questionnaire were used in the final analysis, which eliminated unfaithful responses. The data collected through the survey were analyzed as frequency analysis, reliability analysis, confirmed factor analysis, and structured model using statistical programs SPSS22 and AMOS22. The results of the research identified through the empirical analysis of this study are as follows. First, the CSV activities of the library's big data have a significant influence on the brand quality and social status. Second, brand quality and social resistance has a significant positive effect on continuous use intention. Third, the influence of the CSV activities in Korean and Chinese libraries has been found to be partly different. Through the conclusion and discussion section, the theoretical implications of this study, practical implications and in-depth discussions on the limitations of the study and its future direction were presented.

• Journal of KIISE:Databases
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• v.41 no.4
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• pp.209-216
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• 2014

Various characteristics of social network contents such as real-time, people relationship and big data can help to improve personalized recommender systems. Among them, 'people relationship' is a key factor of recommendation, so many personalized recommender systems utilizing it have been proposed. However, existing researches can not reflect personal tendency and are unable to provide precise recommendations in various domains, because they do not consider intimacy among people. In this paper, to solve these problems, we propose PReAmacy, a Personalized Recommendation Algorithm, considering intimacy among users and various characteristics of social network contents. Our experimental results indicate that not only the precision of PReAmacy is higher than that of existing algorithms, but intimacy is of great importance in PReAmacy.

• Journal of Digital Convergence
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• v.15 no.1
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• pp.61-67
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• 2017

In order to establish and operate a rapid and effective disaster safety management system in an emergency situation that threatens the safety of citizens, such as disaster, accident or terrorism, appropriate responses are necessary. An integrated task execution system for rapid response and restoration should be implemented not only by the central ministries related to disaster management and response, but also by local governments, NGO, and individuals, under clear role sharing. In the case of Seoul city, it is urgent to establish an effective disaster management system for preventing and responding to disasters, because of the increasing possibility of natural disasters due to climate change, the threat of terrorism, urban decay and the industrial accidents. From the perspective of governance, this study tried to seek out countermeasures such as disaster response system and command system at disaster site centering on Seoul city government interdepartmental organization system, implementation process and systematization of response procedures.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.349-356
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• 2022

This study aims to identify factors related to suicide within one year of schizophrenia diagnosis based on data from National Health Information Database. We obtained data of 102,540 patients diagnosed with schizophrenia between 2007 to 2010 from customized database, which was checked using the National Statistics Organization database and schizophrenia cohort was built. The number of suicide within one year of schizophrenia diagnosis was 615(0.60%), and the risk of suicide within one year was high among patients within age group of 25-34 and patients with medium-low to high economic status. The risk of suicide within one year among male patients was high within age group of 45-54 and patients with medium-low to high economic status. The risk of suicide within one year among female patients was high within patients with high economic status. Age and economic status need to be considered during suicide prevention intervention of schizophrenia patients diagnosed within one year and suicide related factors by sex need to be especially considered.