• Korea Trade Review
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• v.44 no.4
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• pp.67-85
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• 2019

This study analyzes the trade issues and curriculum issues of universities in the 4th Industrial Revolution era with the aim of finding strategies to improve the curriculum of international commerce and to cultivate trade manpower by matching them with the trade job competencies required by trade enterprises. To this end, trade college students, GTEP partners, industry-academia partners, and expert groups of N university were asked to provide information on trade curriculum for the current curriculum. The resulting data were analyzed by questionnaire frequency analysis and FGI method to reveal that both students and graduates are interested in improving the trade curriculum of the university, and that companies are also demanding talents who are responsible for the comprehensive process of trade practice and can perform sincerely and comprehensively. Therefore, we have established a new curriculum that is suitable for the 4th industrial age, opened a certificate acquisition course suitable for the needs of the company, and developed the commercial practice, trade simulation, capstone design, and PBL teaching method. Ways are suggesting to reduce mismatch between universities and companies.

• Genomics & Informatics
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• v.21 no.1
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• pp.12.1-12.8
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• 2023

A wave of new technologies has created opportunities for the cost-effective generation of high-throughput profiles of biological systems, foreshadowing a "data-driven science" era. The large variety of data available from biological research is also a rich resource that can be used for innovative endeavors. However, we are facing considerable challenges in big data deposition, integration, and translation due to the complexity of biological data and its production at unprecedented exponential rates. To address these problems, in 2020, the Korean government officially announced a national strategy to collect and manage the biological data produced through national R&D fund allocations and provide the collected data to researchers. To this end, the Korea Bioinformation Center (KOBIC) developed a new biological data repository, the Korea BioData Station (K-BDS), for sharing data from individual researchers and research programs to create a data-driven biological study environment. The K-BDS is dedicated to providing free open access to a suite of featured data resources in support of worldwide activities in both academia and industry.

• Smart Media Journal
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• v.8 no.4
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• pp.46-52
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• 2019

The size of data generated and collected at industrial sites or in public institutions is growing rapidly. The existing data processing server often handles the increasing data by increasing the performance by scaling up. However, in the big data era, when the speed of data generation is exploding, there is a limit to data processing with a conventional server. To overcome such limitations, a distributed cluster computing system has been introduced that distributes data in a scale-out manner. However, because distributed cluster computing systems distribute data, inefficient use of network bandwidth can degrade the performance of the cluster as a whole. In this paper, we propose a scheme that compresses data when transmitting data in a Hadoop cluster considering network bandwidth. The proposed scheme considers the network bandwidth and the characteristics of the compression algorithm and selects the optimal compression transmission scheme before transmission. Experimental results show that the proposed scheme reduces data transfer time and size.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.87-99
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• 2013

Massive increases in data available to an organization are creating a new opportunity for competitive advantage. In this era of big data, developing analytics capabilities, therefore, becomes critical to take advantage of internal and external data and gain insights for data-driven decision making. However, the use of data in education is in its infancy, in comparison with business and government, and the potential for data analytics to impact education services is growing. In this paper, I survey how universities are currently using education data to improve students' performance and administrative efficiency, and propose new ways of extending the current use. In addition, with the so-called data scientist shortage, universities should be able to train professionals with data analytics skills. This paper discusses which skills are valuable to data scientists and introduces various training and certification programs offered by universities and industry. I finally conclude the paper by exploring new curriculums where students, by themselves, can learn how to find and use relevant data even in any courses.

• The Journal of the Korea Contents Association
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• v.19 no.5
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• pp.553-561
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• 2019

In this study, the method of quality measurement for the statistical usefulness of de-identified data was examined in terms of prediction accuracy by statistical modeling. In the era of the 4th industrial revolution, effective use of big data is essential to innovation through information and communication technology, but personal information issues are constrained to actively utilize big data. In order to solve this problem, de-identification guidelines have been established and the possibility of actual re-identification of personal information has become very low due to the utilization of various de-identification methods. On the other hand, strong de-identification can have side effects that degrade the usefulness of the data. We have studied the quality of statistical usefulness of the de-identified data by KLT model which is a representative de-identification method, A case study was conducted to see how statistical accuracy of prediction is degraded by de-identification. We also proposed a new measure of data usefulness of the de-identified data by quantifying how much data is added to the de-identified data to restore the accuracy of the predictive model.

• Journal of the Korean Society for information Management
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• v.35 no.1
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• pp.157-182
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• 2018

As data management and processing techniques have been developed rapidly in the era of big data, nowadays a lot of business companies and researchers have been interested in long tail data which were ignored in the past. This study proposes methods for generating and controlling a network of technical terms based on text mining technique to enhance data utilization in the distribution of long tail theory. Especially, an edit distance technique of text mining has given us efficient methods to automatically create an interlinking network of technical terms in the scholarly field. We have also used linked open data system to gather experimental data to improve data utilization and proposed effective methods to use data of LOD systems and algorithm to recognize patterns of terms. Finally, the performance evaluation test of the network of technical terms has shown that the proposed methods were useful to enhance the rate of data utilization.

• Journal of Digital Convergence
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• v.11 no.6
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• pp.103-112
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• 2013

Traditional method of establishing prediction model is usually using formal data stored in Data Base. However, nowadays advent of "smart" era brought by ground-breaking development of communication system makes informal data to dominate overall data, such 80% in total. Therefore, conventional method using formal data as establishing predicting model would be untrustworthy means in present. In other words, it is indispensible to make prediction model credible including informal data(SNS, image, video) and semi-formal data(log data). In this study, we increase credibility of predicting model adapting Bigdata method and comparing reliability of conventional measurement to real-data.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.517-525
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• 2022

In the era of big data, various data mining techniques have been proposed as major analysis methodologies. As complex and diverse data is mass-produced, data mining techniques have attracted attention as a method that forms the foundation of data science. In this paper, we focused on the decision tree, which is frequently used in practice and easy to understand as one of representative data mining methods. Specifically, we analyzed the effect of the splitting method of decision trees on the model performance. We compared the prediction power and structures of decision tree models with different split methods based on various simulated data. The results show that the linear combination split method can improve the prediction accuracy of decision trees in the case of data simulated from nonlinear models with complex structure.

• Journal of Convergence for Information Technology
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• v.11 no.12
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• pp.1-13
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• 2021

This study tried to suggest ways to improve the indicator system to strengthen the personal information protection. For this purpose, the components of indicator system are derived through domestic and foreign literature, and it was selected as main the diagnostic indicators through FGI/Delphi analysis for personal information protection experts and a survey for personal information protection officers of public institutions. As like this, this study was intended to derive an inspection standard that can be reflected as a separate index system for personal information protection, by classifying the specific IT technologies of the 4th industrial revolution, such as big data, cloud, Internet of Things, and artificial intelligence. As a result, from the planning and design stage of specific technologies, the check items for applying the PbD principle, pseudonymous information processing and de-identification measures were selected as 2 common indicators. And the checklists were consisted 2 items related Big data, 5 items related Cloud service, 5 items related IoT, and 4 items related AI. Accordingly, this study expects to be an institutional device to respond to new technological changes for the continuous development of the personal information management level diagnosis system in the future.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.275-281
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• 2021

Much of our lives are closely related to artificial intelligence, and society is changing more rapidly. Reflecting this era, the need for artificial intelligence education has emerged and various learning methods have been proposed, but guidance on artificial intelligence teaching and learning activities for lower grades elementary school students is insufficient. Therefore, in this study, the data collection education program for the lower grades of elementary school was developed based on the contents standards of the Korea Foundation for the Advancement of Science & Creativity. Focusing on the principles of artificial intelligence and the detailed data area of the utilization area, the focus was on expressing numbers and letters in various ways, such as colors and pictures, and finding various types of data in life to learn the principles of artificial intelligence. Through this program, it is expected that lower-grade elementary school students will be able to understand the importance of data collection in artificial intelligence through the process of knowing about data and collecting sound, picture, and text data.