• Journal of the Korean Society for information Management
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• v.35 no.3
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• pp.77-100
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• 2018

The objectives of this study is to present a discovering process of interdisciplinary convergence technology using text mining of big data. For the convergence research of biotechnology(BT) and information communications technology (ICT), the following processes were performed. (1) Collecting sufficient meta data of research articles based on BT terminology list. (2) Generating intellectual structure of emerging technologies by using a Pathfinder network scaling algorithm. (3) Analyzing contents with topic modeling. Next three steps were also used to derive items of BT-ICT convergence technology. (4) Expanding BT terminology list into superior concepts of technology to obtain ICT-related information from BT. (5) Automatically collecting meta data of research articles of two fields by using OpenAPI service. (6) Analyzing contents of BT-ICT topic models. Our study proclaims the following findings. Firstly, terminology list can be an important knowledge base for discovering convergence technologies. Secondly, the analysis of a large quantity of literature requires text mining that facilitates the analysis by reducing the dimension of the data. The methodology we suggest here to process and analyze data is efficient to discover technologies with high possibility of interdisciplinary convergence.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.625-637
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• 2020

In order to decide the naval ship weapon system acquisition for national policy/market economy activities, the decision makers can determine policy based on current technology level/concentration/utilization. For this, the decision makers apply the major common technology field analysis using patents data. As a method for collecting patent data, we can collect patent data of domestic mobile carriers through the Korea Intellectual Property Rights Information System of Korean Intellectual Property Office. As a result, we collected 14,964 patents/352 International Patent Classification(IPC) types. Based on these data, we performed three analysis processes (SNA, PCA, ARIMA, Text Mining) and got each result from extracting 58 IPC types of SNA and 7 IPC types of PCA. Based on the analysis results, we have confirmed that 7 IPC(B63B, H01M, F03D, B01D, H02K, B23K, H01H) types are the Major Common Technology Distribution of domestic Defense Companies.

• Journal of the Korea Institute of Building Construction
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• v.24 no.1
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• pp.121-131
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• 2024

This research explores the potential of leveraging unstructured data from construction supervision documents, which contain detailed inspection insights from independent third-party monitors of building construction processes. With the evolution of analytical methodologies, such unstructured data has been recognized as a valuable source of information, offering diverse insights. The study introduces a framework designed to assess cost performance by applying advanced analytical methods to the unstructured data found in final construction supervision reports. Specifically, key phrases were identified using text mining and social network analysis techniques, and these phrases were then analyzed through binomial logistic regression to assess cost performance. The study found that predictions of cost performance based on unstructured data from supervision documents achieved an accuracy rate of approximately 73%. The findings of this research are anticipated to serve as a foundational resource for analyzing various forms of unstructured data generated within the construction sector in future projects.

• Journal of Technology Innovation
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• v.23 no.3
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• pp.225-253
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• 2015

The standard is a nation's one of the most important industrial issues that improve the social and economic efficiency and also the basis of the industrial development and trade liberalization. This research analyzes the enactment and the utilization of Korean industrial standards(KS) of various industries. This paper examines Korean industries' KS utilization status based on the KS possession, enactments and inquiry records. First, we implement multidimensional scaling method to visualize and group the KS possession records and the nation's institutional issues. We develop several hypothesis to find the decision factors of how each group's KS possession status impacts on the standard enactment activities of similar industry sectors, and analyzes the data by implementing regression analysis. The results show that the capital intensity, R&D activities and sales revenues affect standardization activities. It suggests that the government should encourage companies with high capital intensity, sales revenues to lead the industry's standard activities, and link the policies with the industry's standard and patent related activities from R&D. Second, we analyze the impacts of each KS data's inquiry records, the year of enactments, the form and the industrial segment on the utilization status by implementing statistical analysis and decision tree method. The results show that the enactment year has significant impact on the KS utilization status and some KSs of specific form and industrial segment have high utilization records despite of short enactment history. Our study suggests that government should make policies to utilize the low-utilized KSs and also consider the utilization of standards during the enactment processes.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.47-64
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• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.41 no.5
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• pp.399-404
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• 2008

This paper proposes revisions in the law governing assessments of the environmental impact of marine projects. In particular, we suggest strengthening the consultative role of the Ministry for Food, Agriculture, Forestry and Fisheries (MIFAFF), the agency responsible for regulating coastal area utilization and development, in order to improve the system by which fishery resources are managed, thus preventing negative environmental impact. Moreover, such an improved impact assessment statement would include reasonable evaluations of the dispersion of pollutants, such as the suspended solids generated by construction projects, as well as of the dredging, dumping and sand mining involved in coastal area reclamation. Thus, public confidence in the latter would be increased by the development and implementation of standardized and consistent guidelines addressing environmental research, simulation processes and evaluations of data.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.1
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• pp.149-158
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• 2019

Due to the rapid development of IT (information technology) and internet, products become smart and able to collect, process and produce information and can think of themselves to provide better service to consumers. However, research on the characteristics of smart product is still sparse. In this paper, we report the systemic development of a scale to measure the perceived product smartness associated with smart product. To develop product smartness scale, this study follows systemic scale development processes of item generation, item reduction, scale validation, reliability and validity test consequently. And, after acquiring a large amount of qualitative interview data asking the definition of smart product, we add a unique process to reduce the initial items using both a text mining method using 'r' s/w and traditional reliability and validity tests including factor analysis. Based on an initial qualitative inquiry and subsequent quantitative survey, an eight-factor scale of product smartness is developed. The eight factors are multi-functionality, human-like touch, ability to cooperate, autonomy, situatedness, network connectivity, integrity, and learning capability consequently. Results from Korean samples support the proposed measures of product smartness in terms of reliability, validity, and dimensionality. Implications and directions for further study are discussed. The developed scale offers important theoretical and pragmatic implications for researchers and practitioners.

• Journal of Intelligence and Information Systems
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• v.13 no.2
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• pp.15-26
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• 2007

In this study, a SOHO (Small Office Home Office) bankruptcy prediction model is proposed using Modified Bagging Predictors which is modification of traditional Bagging Predictors. There have been several studies on bankruptcy prediction for large and middle size companies. However, little studies have been done for SOHOs. In commercial banks, loan approval processes for SOHOs are usually less structured than those for large and middle size companies, and largely depend on partial information such as credit scores. In this study, we use a real SOHO loan approval data set of a Korean bank. First, decision tree induction techniques and artificial neural networks are applied to the data set, and the results are not satisfactory. Bagging Predictors which has been not previously applied for bankruptcy prediction and Modified Bagging Predictors which is proposed in this paper are applied to the data set. The experimental results show that Modified Bagging Predictors provides better performance than decision tree inductions techniques, artificial neural networks, and Bagging Predictors.

• The Journal of Bigdata
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• v.7 no.1
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• pp.213-224
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• 2022

In logistics and distribution, Market Basket Analysis (MBA) is used as an important means to analyze the correlation between major sales products and to increase internal operational efficiency. In particular, the results of market basket analysis are used as important reference data for decision-making processes such as product purchase prediction, product recommendation, and product display structure in stores. With the recent development of e-commerce, the number of items handled by a single distribution and logistics company has rapidly increased, And the existing analytical methods such as Apriori and FP-Growth have slowed down due to the exponential increase in the amount of calculation and applied to actual business. There is a limit to examining important association rules to overcome this limitation, In this study, at the Main-Category level, which is the highest classification system of products, the utility item set mining technique that can consider the sales volume of products together was used to first select a group of products mainly sold together. Then, at the sub-category level, the types of products sold together were identified using FP-Growth. By using this sequential layer filtering technique, it may be possible to reduce the unnecessary calculations and to find practically usable rules for enhancing the effectiveness and profitability.

• Journal of Korea Technology Innovation Society
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• v.19 no.3
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• pp.511-544
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• 2016

Standards boost technological innovation by promoting information sharing, compatibility, stability and quality. Identifying groups of companies that particularly benefit from these functions of standards in their technological innovation and commercialization helps to customize planning and implementation of standards-related policies for demand groups. For this purpose, this study engages in profiling of SMEs whose R&D objective is to respond to standards as well as those who need to implement standards system for technological commercialization. Then it suggests a prediction model that can distinguish such companies from others. To this end, decision tree analysis is conducted for profiling of characteristics of subject SMEs through data mining. Subject SMEs include (1) those that engage in R&D to respond to standards (Group1) or (2) those in need of product standard or technological certification policies for commercialization purposes (Group 2). Then the study proposes a prediction model that can distinguish Groups 1 and 2 from others based on several variables by adopting discriminant analysis. The practicality of discriminant formula is statistically verified. The study suggests that Group 1 companies are distinguished in variables such as time spent on R&D planning, KoreanStandardIndustryClassification (KSIC) category, number of employees and novelty of technologies. Profiling result of Group 2 companies suggests that they are differentiated in variables such as KSIC category, major clients of the companies, time spent on R&D and ability to test and verify their technologies. The prediction model proposed herein is designed based on the outcomes of profiling and discriminant analysis. Its purpose is to serve in the planning or implementation processes of standards-related policies through providing objective information on companies in need of relevant support and thereby to enhance overall success rate of standards-related projects.