• The Journal of the Convergence on Culture Technology
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• v.5 no.2
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• pp.361-366
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• 2019

The amount of data created in the present intelligence information society is beyond imagination. Big data has a great diversity from every information via SNS and internet to the one created by government and enterprises. This various data is close at hand having infinite value as same as crude oil. Big data analysis and utilization by data mining over every areas in the modern industrial society is getting more important for finding useful correlation and strengthening forecasting power against the future uncertainty. Efficient management and utilization of big data produced by complex modern society will be researched in this paper. Also it addresses strategies and methods for securing overall industrial competitiveness, synergy creation among industries, cost reduction and effective application based on big data in the $4^{th}$ industrial revolution era.

• Journal of Creative Information Culture
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• v.6 no.1
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• pp.15-21
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• 2020

In order to utilize big data in general industrial sites, the structured big data collected from facilities, processes, and environments of industrial sites must first be processed and stored, and in the case of unstructured data, it must be stored as unstructured data or converted into structured data and stored in a database. In this paper, we study a method of collecting big data based on open IoT standards that can converge and utilize measurement information, environmental information of industrial sites to collect big data. The platform for collecting big data proposed in this paper is capable of collecting, processing, and storing big data at industrial sites to process real-time sensing information. For processing and analyzing data according to the purpose of the stored industrial, various big data technologies also can be applied.

• Korean small business review
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• v.42 no.4
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• pp.235-250
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• 2020

This study surveyed the perception of university students about employment in Small and Medium-sized Enterprises(SME) in the Chungbuk area to prepare improvement measures. In particular, the data were collected in descriptive questions along with the existing survey methods, and the perception of SME and decent work was identified using text-mining. As a result of the analysis, there are positive perceptions of jobs at SME such as various work experiences and low job competition rates, while there are generally many negative perceptions in pay, work and welfare. However, as a result of co-occurrence network analysis of responses to decent jobs, 'Information' was derived as a keyword. Currently, college students' negative perception of SME is affected by the lack of sufficient information, which needs to be improved first. To solve this problem, it was proposed to establish and operate a platform that can provide information on employment of SME and select necessary personnel.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.41-54
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• 2023

Smart grid that supports efficient energy production and management is used in various fields and industries. However, because of the environment in which services are provided through open networks, it is essential to resolve trust issues regarding security vulnerabilities and privacy preservation. In particular, the identification information of smart meter is managed by a centralized server, which makes it vulnerable to security attacks such as device stolen, data forgery, alteration, and deletion. To solve these problems, this paper proposes a blockchain based authentication protocol for a smart meter. The proposed scheme issues an unique decentralized identifiers (DIDs) for individual smart meter through blockchain and utilizes a random values based on physical unclonable function (PUF) to strengthen the integrity and reliability of data. In addition, we analyze the security of the proposed scheme using informal security analysis and AVISPA simulation, and show the efficiency of the proposed scheme by comparing with related work.

• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.65-70
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• 2024

As the interest in AI education grows, researchers have made efforts to implement AI education programs. However, research targeting pre-service teachers has been limited thus far. Therefore, this study was conducted to develop an AI literacy education program for preservice secondary teachers. The research results revealed that the weekly topics included the definition and applications of AI, analysis of intelligent agents, the importance of data, understanding machine learning, hands-on exercises on prediction and classification, hands-on exercises on clustering and classification, hands-on exercises on unstructured data, understanding deep learning, application of deep learning algorithms, fairness, transparency, accountability, safety, and social integration. Through this research, it is hoped that AI literacy education programs for preservice teachers will be expanded. In the future, it is anticipated that follow-up studies will be conducted to implement relevant education in teacher training institutions and analyze its effectiveness.

• The Transactions of the Korea Information Processing Society
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• v.13 no.9
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• pp.453-459
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• 2024

Recent advances in generative AI technologies using natural language processing have critically impacted text, image, and video production. Despite these innovations, we still need to improve the consistency and reusability of AI-generated outputs. These issues are critical in cartoon creation, where the inability to consistently replicate characters and specific objects can degrade the work's quality. We propose an integrated adaption of language analysis-based requirement engineering and cartoon engineering to solve this. The proposed method applies the linguistic frameworks of Chomsky and Fillmore to analyze natural language and utilizes UML sequence models for generating consistent 3D representations of object interactions. It systematically interprets the creator's intentions from textual inputs, ensuring that each character or object, once conceptualized, is accurately replicated across various panels and episodes to preserve visual and contextual integrity. This technique enhances the accuracy and consistency of character portrayals in animated contexts, aligning closely with the initial specifications. Consequently, this method holds potential applicability in other domains requiring the translation of complex textual descriptions into visual representations.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.3
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• pp.13-24
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• 2018

For government and public institutions, civil complaints containing direct requirements of citizens can be utilized as important data in developing policies. However, it is difficult to draw accurate requirements using text mining methods since the nature of the complaint text is unstructured. In this study, a new method is proposed that draws the exact requirements of citizens, improving the previous text mining in analyzing the data of civil complaints. The new text-mining method is based on the principle of Co-Occurrences Structure Map, and it is structured by two-step association analysis, so that it consists of the first-order related word and a second-order related word based on the core subject word. For the analysis, 3,004 cases posted on the electronic bulletin board of Busan City for the year 2016 are used. This study's academic contribution suggests a method deriving the requirements of citizens from the civil affairs data. As a practical contribution, it also enables policy development using civil service data.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.133-143
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• 2017

Data is the most important asset in the financial sector. On average, 71 percent of financial institutions generate competitive advantage over data analysis. In particular, in the card industry, the card transaction data is widely used in the development of merchant information, economic fluctuations, and information services by analyzing patterns of consumer behavior and preference trends of all customers. However, creation of new value through fusion of data is insufficient. This study introduces the analysis and forecasting of consumption trends of credit card companies which convergently analyzed the social data and the sales data of the company's own. BC Card developed an algorithm for linking card and social data with trend profiling, and developed a visualization system for analysis contents. In order to verify the performance, BC card analyzed the trends related to 'Six Pocket' and conducted th pilot marketing campaign. As a result, they increased marketing multiplier by 40~100%. This study has implications for creating a methodology and case for analyzing the convergence of structured and unstructured data analysis that have been done separately in the past. This will provide useful implications for future trends not only in card industry but also in other industries.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.87-94
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• 2019

In this paper, the method of analyzing the unformatted data of consumers accumulated on social networks in the era of the Fourth Industrial Revolution by utilizing data from the service design and social psychology aspects was proposed. First, the fandom phenomenon, which shows subjective and collective behavior in a space on a social network rather than physical space, was defined from a data service perspective. The fandom model has been transformed into a collective level of customer Persona that has been analyzed at a personal level in traditional service design, and social network analysis that analyzes consumers' big data has been presented as an efficient way to pattern and visually analyze it. Consumer data collected through social leasing were pre-processed by column based on correlation, stability, missing, and ID-ness. Based on the above data, the company's brand strategy was divided into active and passive interventions and the effect of this strategic attitude on the growth direction of the consumer's fandom community was analyzed. To this end, the fandom model of consumers was proposed by dividing it into four strategies that the brand strategy had: stand-alone, decentralized, integrated and centralized, and the fandom shape of consumers was proposed as a growth model analysis technique that analyzes changes over time.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.1-36
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• 2020

This study is to reveal the acceptance factors of the Market Sentiment Index (MSI) created by reflecting the investor sentiment extracted by processing unstructured big data. The research model was established by exploring exogenous variables based on the rational behavior theory and applying the Technology Acceptance Model (TAM). The acceptance of MSI provided to investors in the stock market was found to be influenced by the exogenous variables presented in this study. The results of causal analysis are as follows. First, self-efficacy, investment opportunities, Innovativeness, and perceived cost significantly affect perceived ease of use. Second, Diversity of services and perceived benefits have a statistically significant impact on perceived usefulness. Third, Perceived ease of use and perceived usefulness have a statistically significant effect on attitude to use. Fourth, Attitude to use statistically significantly influences the intention to use, and the investment opportunities as an independent variable affects the intention to use. Fifth, the intention to use statistically significantly affects the final dependent variable, the intention to use continuously. The mediating effect between the independent and dependent variables of the research model is as follows. First, The indirect effect on the causal route from diversity of services to continuous use intention was 0.1491, which was statistically significant at the significance level of 1%. Second, The indirect effect on the causal route from perceived benefit to continuous use intention was 0.1281, which was statistically significant at the significance level of 1%. The results of the multi-group analysis are as follows. First, for groups with and without stock investment experience, multi-group analysis was not possible because the measurement uniformity between the two groups was not secured. Second, the analysis result of the difference in the effect of independent variables of male and female groups on the intention to use continuously, where measurement uniformity was secured between the two groups, In the causal route from usage attitude to usage intention, women are higher than men. And in the causal route from use intention to continuous use intention, males were very high and showed statistically significant difference at significance level 5%.