• Journal of Fashion Business
• /
• v.24 no.4
• /
• pp.144-160
• /
• 2020

Today, the development of innovative technologies is accompanied by changes in the industrial structure and the Big Blur phenomenon, where the boundaries in various fields are blurred. The purpose of this study was to view the Big Blur phenomenon as a big paradigm shift in the 21st century and derive environmental changes and characteristics of the Korean fashion market. The research method included an analysis of the fashion brands after 2015. Through this study, we intended to establish a framework for understanding the changes in the fashion market from the perspective of Big Blur and discuss the direction of brand marketing. The research results showed the hyperlinks, connectivity, openness, homeostasis, synchronicity, mobility, interactivity, and brand experience of online and offline spaces beyond the boundaries of virtual space and offline physical spaces such as online physical and spatial viewpoints. It also showed the characteristics. The characteristics from the socio-cultural point of view were characteristic of diversity, mixture, coexistence, composability, and pluralism beyond the traditional socio-cultural and regulatory scopes. Hip hop fashion, street fashion, unisex, genderless, androgynous fashion, and kid fashion are the backbone of the Big Blur and are becoming important factors in fashion. The characteristics of the market and economic viewpoint are prosumers that play roles both as producers and consumers. It shows the extensibility of consumers as producers, the cohesiveness of producers and consumers, the cooperation, and the interconnectivity.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.15 no.4
• /
• pp.197-212
• /
• 2019

The purpose of this study is to identify the maturity stages of venture firms through classification analysis, which is widely used as a big data technique. Venture companies should develop a competitive advantage in the market. And the maturity stage of a company can be classified into five stages. I will analyze a difference in the growth stage of venture firms between the survey response and the statistical classification methods. The firm growth level distinguished five stages and was divided into the period of start-up and declines. A classification method of big data uses popularly k-mean cluster analysis, hierarchical cluster analysis, artificial neural network, and decision tree analysis. I used variables that asset increase, capital increase, sales increase, operating profit increase, R&D investment increase, operation period and retirement number. The research results, each big data analysis technique showed a large difference of samples sized in the group. In particular, the decision tree and neural networks' methods were classified as three groups rather than five groups. The groups size of all classification analysis was all different by the big data analysis methods. Furthermore, according to the variables' selection and the sample size may be dissimilar results. Also, each classed group showed a number of competitive differences. The research implication is that an analysts need to interpret statistics through management theory in order to interpret classification of big data results correctly. In addition, the choice of classification analysis should be determined by considering not only management theory but also practical experience. Finally, the growth of venture firms needs to be examined by time-series analysis and closely monitored by individual firms. And, future research will need to include significant variables of the company's maturity stages.

• The Journal of the Convergence on Culture Technology
• /
• v.7 no.2
• /
• pp.351-357
• /
• 2021

At the time when interest in the use of big data is rising in the face of the technological paradigm shift of the 4th industrial revolution, interest in the use of patented big data is increasing, especially as the proportion of intangible assets of companies increases. In addition to quantitative information, patent data contains various information such as unstructured text such as title, abstract, claim, citation and citation relations, drawings, and technology classification. It is judged that the use of treatment is important. Therefore, in this study, in order to systematically operate the innovative patent strategy framework and to secure a competitive advantage by strengthening the fundamental technological competitiveness of the company, we propose a method of using patent big data centering on the case of Company A, and verify its validity. I would like to suggest some implications. Through this, it is intended to raise awareness of the use of patent big data, and to suggest ways to use patent big data in connection with the company's company-wide strategy, business strategy, and functional strategy.

• Journal of the Korean Society of Industry Convergence
• /
• v.22 no.6
• /
• pp.775-783
• /
• 2019

The big data industry market continues to grow and is expected to grow further. In this paper, based on the five-point Likert scale of college students in the process of developing big data young people, the satisfaction of instructors in big data training and improvement of job (education) ability based on AI convergence The survey was conducted on the expectations of the participants and their intention to participate in the training process for the young talents. Male students were more satisfied than students. In terms of students, students who are less than 6th semester have the highest satisfaction, but students who are less than 7th and 8th semesters are less satisfied. By department, the satisfaction level of science and statistics students was the highest, while the satisfaction level of other students was low. According to the average of college credits, the satisfaction of students under 3.5~4.0 was the highest, and the satisfaction of students below 3.0 was the lowest.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
• /
• v.23 no.3
• /
• pp.201-209
• /
• 2017

The Big Data, the element of the Fourth Industrial Revolution, is drawing attention as the 4th Industrial Revolution is mentioned in the 2016 World Economic Forum. Big Data is being used in various fields because it predicts the near future and can create new business. However, utilization and research in the field of packaging are lacking. Today packaging has been demanded marketing elements that effect on consumer choice. Big data is actively used in marketing. In the marketing field, big data can be used to analyze sales information and consumer reactions to produce meaningful results. Therefore, this study proposed a method of applying big data in the field of packaging focusing on marketing. In this study suggest that try to utilize the private data and community data to analyze interaction between consumers and products. Using social big data will enable to understand the preferred packaging and consumer perceptions and emotions in the same product line. It can also be used to analyze the effects of packaging among various components of the product. Packaging is one of the many components of the product. Therefore, it is not easy to understand the impact of a single packaging element. However, this study presents the possibility of using Big Data to analyze the perceptions and feelings of consumers about packaging.

• Journal of Korean Society of Archives and Records Management
• /
• v.21 no.4
• /
• pp.1-18
• /
• 2021

Today, record management has become more important in management as records generated from administrative work and data production have increased significantly, and the development of information and communication technology, the working environment, and the size and various functions of the government have expanded. It is explained as an example in connection with the concept of public records with the characteristics of big data and big data characteristics. Social, Technological, Economical, Environmental and Political (STEEP) analysis was conducted to examine such areas according to the big data generation environment. The appropriateness and necessity of applying big data technology in the field of public record management were identified, and the top priority applicable framework for public record management work was schematized, and business implications were presented. First, a new organization, additional research, and attempts are needed to apply big data analysis technology to public record management procedures and standards and to record management experts. Second, it is necessary to train record management specialists with "big data analysis qualifications" related to integrated thinking so that unstructured and hidden patterns can be found in a large amount of data. Third, after self-learning by combining big data technology and artificial intelligence in the field of public records, the context should be analyzed, and the social phenomena and environment of public institutions should be analyzed and predicted.

• Journal of the Korea Convergence Society
• /
• v.10 no.6
• /
• pp.203-218
• /
• 2019

Using the sample of 104 mutual savings banks inspected by the Financial Supervisory Service (FSS) on June 2011, this study examines if mutual savings banks manage BIS capital adequacy ratio using allowance for bad debts through comparison of BIS capital adequacy ratio before and after the 2011 when mutual savings banks experienced a large-scale restructuring by financial supervisory authorities. We find that mutual savings banks mainly use the allowance for bad debts to manage BIS capital adequacy ratio. It also shows that mutual savings banks with a business suspension order by FSS manage BIS capital adequacy ratio more than the others. Lastly, we find that Non Big4 auditors as well as Big 4 auditors don't effectively audit the use of the allowance for bad debts for mutual savings banks to manage their BIS capital adequacy ratio.

• Journal of Convergence for Information Technology
• /
• v.8 no.5
• /
• pp.145-152
• /
• 2018

The paper aims to present the direction in which accounting education should proceed to enhance the expertise of accounting works in the new era in which big data is the center. This paper examines the definition and analysis of big data, and reviews the effectiveness through big data development in accounting expertise with specific references. Also, this paper presents some of the plans selected by professional accounting bodies and universities to address the topic of big data in the accounting curriculum. According to the plan, big data could provide a blueprint for the future role of accounting and financial experts. Therefore, what this study suggests is to improve educational content by adding big data topics to current accounting curricula in order to help accounting professionals of future generations prepare for technologies related to big data analysis in advance.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.14 no.4
• /
• pp.256-267
• /
• 2014

An automatic switch among ensembles of clustering algorithms is proposed as a part of the bibliographic big data retrieval system by utilizing a fuzzy inference engine as a decision support tool to select the fastest performing clustering algorithm between fuzzy C-means (FCM) clustering, Newman-Girvan clustering, and the combination of both. It aims to realize the best clustering performance with the reduction of computational complexity from O($n^3$) to O(n). The automatic switch is developed by using fuzzy logic controller written in Java and accepts 3 inputs from each clustering result, i.e., number of clusters, number of vertices, and time taken to complete the clustering process. The experimental results on PC (Intel Core i5-3210M at 2.50 GHz) demonstrates that the combination of both clustering algorithms is selected as the best performing algorithm in 20 out of 27 cases with the highest percentage of 83.99%, completed in 161 seconds. The self-adapted FCM is selected as the best performing algorithm in 4 cases and the Newman-Girvan is selected in 3 cases.The automatic switch is to be incorporated into the bibliographic big data retrieval system that focuses on visualization of fuzzy relationship using hybrid approach combining FCM and Newman-Girvan algorithm, and is planning to be released to the public through the Internet.

• Journal of Internet Computing and Services
• /
• v.21 no.2
• /
• pp.27-37
• /
• 2020

Recently, big data is rapidly emerging as a core technology in the 4th industrial revolution. Further, the utilization and the demand of drones are continuously increasing with the development of the 4th industrial revolution. However, as the drones usage increases, the risk of drones falling increases. Drones always have a risk of being able to fall easily even with small problems due to its simple structure. In this paper, in order to predict the risk of drone fall and to prevent the fall, ESC (Electronic Speed Control) is attached integrally with the drone's driving motor and the acceleration sensor is stored to collect the vibration data in real time. By processing and monitoring the data in real time and analyzing the data through big data obtained in such a situation using a Fast Fourier Transform (FFT) algorithm, we proposed a prediction system that minimizes the risk of drone fall by analyzing big data collected from drones.