• KIPS Transactions on Software and Data Engineering
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• v.5 no.6
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• pp.295-306
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• 2016

The purpose of this paper is both to analyze research trends and to predict industrial flows using the meta-data from the previous studies on data quality. There have been many attempts to analyze the research trends in various fields till lately. However, analysis of previous studies on data quality has produced poor results because of its vast scope and data. Therefore, in this paper, we used a text mining, social network analysis for time series network analysis to analyze the vast scope and data of data quality collected from a Web of Science index database of papers published in the international data quality-field journals for 10 years. The analysis results are as follows: Decreases in Mathematical & Computational Biology, Chemistry, Health Care Sciences & Services, Biochemistry & Molecular Biology, Biochemistry & Molecular Biology, and Medical Information Science. Increases, on the contrary, in Environmental Sciences, Water Resources, Geology, and Instruments & Instrumentation. In addition, the social network analysis results show that the subjects which have the high centrality are analysis, algorithm, and network, and also, image, model, sensor, and optimization are increasing subjects in the data quality field. Furthermore, the industrial connection analysis result on data quality shows that there is high correlation between technique, industry, health, infrastructure, and customer service. And it predicted that the Environmental Sciences, Biotechnology, and Health Industry will be continuously developed. This paper will be useful for people, not only who are in the data quality industry field, but also the researchers who analyze research patterns and find out the industry connection on data quality.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.593-606
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• 2016

Computer simulation is widely used in a variety of computational science and engineering fields, including computational fluid dynamics, nano physics, computational chemistry, structural dynamics, and computer-aided optimal design, to simulate the behavior of a system. As the demand for the accuracy and complexity of the simulation grows, however, the cost of executing the simulation is rapidly increasing. It, therefore, is very important to lower the total execution time of the simulation especially when that simulation makes a huge number of repetitions with varying values of input parameters. In this paper we develop a simulation service system that provides the ability to predict the result of the requested simulation without actual execution for that simulation: by recording and then returning previously obtained or predicted results of that simulation. To achieve the goal of avoiding repetitive simulation, the system provides two main functionalities: (1) storing simulation-result records into database and (2) predicting from the database the result of a requested simulation using statistical machine learning techniques. In our experiments we evaluate the prediction performance of the system using real airfoil simulation result data. Our system on average showed a very low error rate at a minimum of 0.9% for a certain output variable. Using the system any user can receive the predicted outcome of her simulation promptly without actually running it, which would otherwise impose a heavy burden on computing and storage resources.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.10
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• pp.391-398
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• 2021

Since the leased line is a structure that exclusively uses two connected areas for data transmission, a stable quality level and security are ensured, and despite the rapid increase in the number of switched lines, it is a line method that is continuously used a lot in companies. However, because the cost is relatively high, one of the important roles of the network operator in the enterprise is to maintain the optimal state by properly arranging and utilizing the resources of the network leased line. In other words, in order to properly support business service requirements, it is essential to properly manage bandwidth resources of leased lines from the viewpoint of data transmission, and properly predicting and managing leased line usage becomes a key factor. Therefore, in this study, various prediction models were applied and performance was evaluated based on the actual usage rate data of leased lines used in corporate networks. In general, the performance of each prediction was measured and compared by applying the smoothing model and ARIMA model, which are widely used as statistical methods, and the representative models of deep learning based on artificial neural networks, which are being studied a lot these days. In addition, based on the experimental results, we proposed the items to be considered in order for each model to achieve good performance for prediction from the viewpoint of effective operation of leased line resources.

• The Journal of the Korea Contents Association
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• v.18 no.12
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• pp.535-546
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• 2018

This study conducted a meta-analysis to examine syntagmatically on the variables related with recovery among persons with mental illness in Korea. In order to do a meta-analysis, theses and dissertations published between 1999 and 2018 in Korea were reviewed systematically and a total of 24 including studies were selected. Using Comprehensive Meta Analysis (CMA) 3.0 software, this study calculated average effect size and moderator variables related with recovery among persons with mental illness. Results were as follows. First, this study identified a total of 16 variables related with recovery among persons with mental illness. Second, the results indicated that variables which showed large effect sizes included social support(r=.575), empowerment(r=.555), self-efficacy(r=.544), social skill(r=.500), relationship with social worker(r=.482), stigma(r=-.446), family support(r=.418). Third, variables with medium effect sizes included interpersonal relationship capacity (r=.391), agency service satisfaction(r=.366), insight(r=.373) and symptom(r=-.239). Fourth, variables with small effect sizes included work experience(r=.188). Fifth, moderator analyses were conducted utilizing characteristics of residence state (community or mental hospital). Moderator effects were identified in the social support and family support. Based on the findings, theoretical and clinical implications for the recovery among persons with mental illness in Korea were discussed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.701-713
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• 2021

Race Condition is a vulnerability in which two or more processes input or manipulate a common resource at the same time, resulting in unintended results. This vulnerability can lead to problems such as denial of service, elevation of privilege. When a vulnerability occurs in software, the relevant information is documented, but often the cause of the vulnerability or the source code is not disclosed. In this case, analysis at the binary level is necessary to detect the vulnerability. This paper aims to detect the Time-Of-Check Time-Of-Use (TOCTOU) Race Condition vulnerability of UNIX kernel-based File System at the binary level. So far, various detection techniques of static/dynamic analysis techniques have been studied for the vulnerability. Existing vulnerability detection tools using static analysis detect through source code analysis, and there are currently few studies conducted at the binary level. In this paper, we propose a method for detecting TOCTOU Race Condition in File System based on Control Flow Graph and Call Graph through Binary Analysis Platform (BAP), a binary static analysis tool.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.321-331
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• 2021

This study sought to analyze factors influencing high-risk drinking in single-person households. For this, data from the 2018 community health survey were used. Subjects were 32,389 adults above the age of 19 in single-person households. For the data analysis, high-risk drinking groups were extracted according to the high-risk drinking rate index of the survey to arrive at influencing factors and differences in health-related and sociodemographic characteristics. The IBM SPSS 25.0 software was used for analysis and a complex sampling design was applied. The results showed that the high-risk drinking rate of Korea's single-person households was 15.0% (male: 25.8%, female: 5.8%) and age, education under high school level, service-industry employees, smokers, people with depression, high blood pressure, and irregular breakfast eaters appeared as common elements for both genders. Stress appeared to only affect males while being diabetic only affected females. High-risk drinking was higher for males in their 30~40s and women in their 20~30s. The younger generation showed the highest numbers in high-risk drinking and factors like stress or depression appeared to be influencing factors for high-risk drinking. Hence, mental health programs along with customized health policies through health forms and lifestyle changes will be required to lower the high-risk drinking rates of single-person households.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.2
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• pp.102-116
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• 2021

For the safe operation of autonomous vehicles, the representative technology of the 4^th industrial revolution era, a combination of various technologies such as sensor technology, software technology and car technology is required. An autonomous vehicle is a vehicle that recognizes current location and situation by using the various sensors, and makes its own decisions without depending on the driver. Perfect recognition technology is required for fully autonomous driving. Since the precise road maps provide various road information including lanes, stop lines, traffic lights and crosswalks, it is possible to minimize the cognitive errors that occur in autonomous vehicles by using the precise road maps with location information of the road facilities. In this study, the definition, necessity and technical trends of the precise road map have been analyzed, and the HD(High Definition) map prototype based on the web GIS has been built in the autonomous driving-specialized areas of Daegu Metropolitan City(Suseong Medical District, about 24km), the Happy City of Sejong Special Self-Governing City(about 33km), and the FMTC(Future Mobility Technical Center) PG(Proving Ground) of Seoul National University Siheung Campus using the MMS(Mobile Mapping System) surveying results given by the National Geographic Information Institute. In future research, the built-in precise road map service will be installed in the autonomous vehicles and control systems to verify the real-time locations and its location correction algorithm.

• Therapeutic Science for Rehabilitation
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• v.11 no.3
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• pp.23-36
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• 2022

Objectives : This study aimed to provide basic data on intervention strategies that occupational therapists can access by systematically analyzing the intervention and effectiveness for youth with disabilities. Methods : The RISS, PubMed, and Web of Science databases were used to search for papers published between 2006 and 2021. The keywords were "Disability AND Adolescents OR Young adult AND Transition education OR Transition program". Seven papers were selected for analysis, and the full text was reviewed. The keywords and national relations were analyzed and visualized using the WoS (Web of Science) and VOSviewer programs. Results : The participants were classified into five types (ASD or ADHD, ID, DD, and physical disability). The areas used for the intervention were mixed into three categories: occupation (academic), self-management (time), and interaction (personal relations and communication). Sociality and adaptation, quality of life, and at least one of the three categories of daily life activities showed significant improvement. Conclusions : This study can be used as basic data to expand the area where only OTs can contribute while grasping the research trend of the conversion program and presenting the direction of exchange with various experts by organizing the application and its effects.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.189-196
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• 2022

Skyline query is a scheme for exploring objects that are suitable for user preferences based on multiple attributes of objects. Existing skyline queries return search results as batch processing, but the need for real-time search results has increased with the advent of interactive apps or mobile environments. Online algorithm for Skyline improves the return speed of objects to explore preferred objects in real time. However, the object navigation process requires unnecessary navigation time due to repeated comparative operations. This paper proposes a Pre-processing Online Algorithm for Skyline Query (POA) to eliminate unnecessary search time in Online Algorithm exploration techniques and provide the results of skyline queries in real time. Proposed techniques use the concept of range-limiting to existing Online Algorithm to perform pretreatment and then eliminate repetitive rediscovering regions first. POAs showed improvement in standard distributions, bias distributions, positive correlations, and negative correlations of discrete data sets compared to Online Algorithm. The POAs used in this paper improve navigation performance by minimizing comparison targets for Online Algorithm, which will be a new criterion for rapid service to users in the face of increasing use of mobile devices.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.149-158
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• 2023

As data utilization is becoming more important recently, the importance of data centers is also increasing. However, the data center is a problem in terms of environment and economy because it is a massive power-consuming facility that runs 24 hours a day. Recently, studies using deep learning techniques to reduce power used in data centers or servers or predict traffic have been conducted from various perspectives. However, the amount of traffic data processed by the server is anomalous, which makes it difficult to manage the server. In addition, many studies on dynamic server management techniques are still required. Therefore, in this paper, we propose a dynamic server management technique based on Long-Term Short Memory (LSTM), which is robust to time series data prediction. The proposed model allows servers to be managed more reliably and efficiently in the field environment than before, and reduces power used by servers more effectively. For verification of the proposed model, we collect transmission and reception traffic data from six of Wikipedia's data centers, and then analyze and experiment with statistical-based analysis on the relationship of each traffic data. Experimental results show that the proposed model is helpful for reliably and efficiently running servers.