• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.

• Journal of agricultural medicine and community health
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• v.31 no.1
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• pp.35-46
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• 2006

Objectives: In recently. the management and protection on individual information in patient's medical & nursing records have been very important, and that need a guideline. The purpose of this study was to investigate the status of using the patient's nursing records of nursing students in clinical practice, to find and discuss the patient's informed consent, and status of education and management concerned to patient's nursing records. Methods: This study used a mailing survey. data collected from September 24th to October 31th in 2002. The subject were 333 professors who are major in adult nursing, pediatric nursing, psychological nursing of 111 university of nursing department and nursing college. And then we received the survey mail from 103 professors that respondent rate was 30.9%. Results: The characteristics of study subjects showed 49.0% of university. 51.0% of college of nursing. 50.0% of the subjects practiced point the patient by oral approval in clinical practice. But when the decision of the patient was very difficult, 21.6% of the subjects take to informed consent from his or her families. During the clinical practice, 49.0% of the subjects were explain to patient about clinical practice and contents of the nursing student, only 7.8% of the subjects were explain to patient with nursing records. 52.0% of the subjects were took out records from the hospital, only 17.6% of the subjects had standard of the patient's informed consent and standard of handling practice records. 17.6%-92.2% of the subjects that educate and manage concern to patient's nursing records.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.77-83
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• 2015

Frequent pattern mining, which is one of the major areas actively studied in data mining, is a method for extracting useful pattern information hidden from large data sets or databases. Moreover, frequent pattern mining approaches have been actively employed in a variety of application fields because the results obtained from them can allow us to analyze various, important characteristics within databases more easily and automatically. However, traditional frequent pattern mining methods, which simply extract all of the possible frequent patterns such that each of their support values is not smaller than a user-given minimum support threshold, have the following problems. First, traditional approaches have to generate a numerous number of patterns according to the features of a given database and the degree of threshold settings, and the number can also increase in geometrical progression. In addition, such works also cause waste of runtime and memory resources. Furthermore, the pattern results excessively generated from the methods also lead to troubles of pattern analysis for the mining results. In order to solve such issues of previous traditional frequent pattern mining approaches, the concept of representative pattern mining and its various related works have been proposed. In contrast to the traditional ones that find all the possible frequent patterns from databases, representative pattern mining approaches selectively extract a smaller number of patterns that represent general frequent patterns. In this paper, we describe details and characteristics of pattern condensing techniques that consider the maximality or closure property of generated frequent patterns, and conduct comparison and analysis for the techniques. Given a frequent pattern, satisfying the maximality for the pattern signifies that all of the possible super sets of the pattern must have smaller support values than a user-specific minimum support threshold; meanwhile, satisfying the closure property for the pattern means that there is no superset of which the support is equal to that of the pattern with respect to all the possible super sets. By mining maximal frequent patterns or closed frequent ones, we can achieve effective pattern compression and also perform mining operations with much smaller time and space resources. In addition, compressed patterns can be converted into the original frequent pattern forms again if necessary; especially, the closed frequent pattern notation has the ability to convert representative patterns into the original ones again without any information loss. That is, we can obtain a complete set of original frequent patterns from closed frequent ones. Although the maximal frequent pattern notation does not guarantee a complete recovery rate in the process of pattern conversion, it has an advantage that can extract a smaller number of representative patterns more quickly compared to the closed frequent pattern notation. In this paper, we show the performance results and characteristics of the aforementioned techniques in terms of pattern generation, runtime, and memory usage by conducting performance evaluation with respect to various real data sets collected from the real world. For more exact comparison, we also employ the algorithms implementing these techniques on the same platform and Implementation level.

• Archives of design research
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• v.12 no.2
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• pp.157-168
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• 1999

Stone in the Cho Sun Dynasty has been used as the material of industry arts, widely building materials and an important material for the artistic design. But it has been generally used as an use of practical living Product like, suban, a metal printing type, a fire place, a pillowcase, a pencil case, etc, and ornament with the quality of stone material and the aesthetic view. There are several problems as like size, quantity, delivery and processing method with the stne material according to its variety and selection. Accordingly it has been studied through the whole process of shape, a material selection of design and processing method as well as studying about small stone crafts which were manufactured in the Cho Sun Dynasty, considering these problems. Stone crafts in Cho Sun Dynasty has been widely used as a living tool. There was some what difference on manufacturing purpose on its technique of folk crafts of stone crafts, but, through the research of collected crafts, they were almost the same that social need, user's taste and hobby in those days were reflected in. In the result of analysis as dividing the stone crafts into daily living product, stationery and tool, the major of daily products have been manufactured with emphasis of practicability. And they have been manufactured from agalmatolite for the propose of use. further, kitchen product had no design due to the function and living products which has been used in the main living room has been carved with the decorative expression of the various form by using intaglic, relieve, inlaid technique, etc. For the stationery, it has been characterized with aesthetic point considering the decorative effects & selection of material in accordance with use. A material for manufacturing has been used in the range of agalmtolite, atopaz, a sapphire, white stone etc. As the result of this research, It was noticeable that there was the spleudidness on the expression of design and carving. It was also noticeable that black stone and guanite have been widely esed because it didn't need the delicacy as a tool.

• Journal of Legislation Research
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• no.53
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• pp.177-211
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• 2017

In the course of the emergence and development of new ICT technologies and services such as Big Data, Internet of Things and Artificial Intelligence, the future will change by these new innovations in the Fourth Industrial Revolution. The future of this fourth industrial revolution will change and our future will be data-based society or economy. Since there is personal information at the center of it, the development of the economy through the utilization of personal information will depend on how to make the personal information protection laws. In Korea, which is trying to lead the 4th industrial revolution, it is a legal interest that can not give up the use of personal information, and also it is an important legal benefit that can not give up the personal interests of individuals who want to protect from personal information. Therefore, it is necessary to change the law on personal information protection in a rational way to harmonize the two. In this regard, this article discusses the problems of duplication and incompatibility of the personal information protection law, the scope of application of the personal information protection law and the uncertainty of the judgment standard, the lack of flexibility responding to the demand for the use of reasonable personal information, And there is a problem of reverse discrimination against domestic area compared to the regulated blind spot in foreign countries. In order to solve these problems and to improve the legislation of personal information protection in the era of the fourth industrial revolution, we proposed to consider both personal information protection and safe use by improving the purpose and regulation direction of the personal information protection law. The balance and harmony between the systematical maintenance of the personal information protection legislation and laws and regulations were also set as important directions. It is pointed out that the establishment of rational judgment criteria and the legislative review to clarify it are necessary for the constantly controversial personal information definition regulation and the method of allowing anonymization information as the intermediate domain. In addition to the legislative review for the legitimate and non-invasive use of personal information, there is a need to improve the collective consent system for collecting personal information to differentiate the subject and to improve the legislation to ensure the effectiveness of the regulation on the movement of personal information between countries. In addition to the issues discussed in this article, there may be a number of challenges, but overall, the protection and use of personal information should be harmonized while maintaining the direction indicated above.

• The Journal of Engineering Geology
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• v.32 no.3
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• pp.411-420
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• 2022

Recently, research using remote sensing has been active in various fields such as environment, science, and society. The results of research using remote sensing are not only numerical results, but also play an important role in solving and preventing social and scientific problems. The purpose of this thesis is to tell the correlation between the data provided and each data by using remote sensing technology for the tidal flat environment. The purpose of this study is to obtain high-resolution data using artificial satellites during remote sensing to find out information on tidal flat currents. Tidal flats created by erosion, sedimentation, low tide, and high tide contain information about the tidal flat slope and information about the ecosystem. Therefore, it can be considered as one of the very important studies to analyze the overall tidal flow channel. This paper creates a DEM (Digital Elevation Model) through TanDEM-X, and DEM is used as the most basic data to create a tidal channel. The research area is a tidal flat located in the middle of the west coast of Ganghwado tidal flat. By analyzing the tidal channel created, various information such as the slope direction of Ganghwado tidal flat and the shape of the tidal channel can be grasped. It is expected that the results of this study will increase the importance and necessity of using DEM data for tidal flat research in the future, and that high-quality results can be obtained.

• Journal of Science Education
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• v.46 no.1
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• pp.93-108
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• 2022

The purpose of this study is to develop a STEAM program that can be used in the high school credit system to be fully implemented in 2025, and to examine its validity and effectiveness. The STEAM program analyzed the 2015 revised curriculum centering on science, technology, and engineering through the 2015 revised curriculum analysis, and then selected the five latest issues: hydrogen fuel, climate crisis, data science, appropriate technology, and barista. In accordance with this self-developed program development format (frame), it was developed for seven months through a process of group deliberation. The draft of the STEAM program for 29 sessions of five types, developed to indirectly experience the career path and occupation of high school students, was verified through consultation with 2 STEAM education experts. It was applied at five different high schools for a pilot implementation. As a result of the pilot application, it was confirmed that the students' STEAM attitude significantly improved in the post-test than the pre-test, and the students' high satisfaction with the program was confirmed. In addition, through an interview with the pilot application teacher, it was positively evaluated that 'the content and level of the program are suitable and through experience solving real-life problems, you can apply the content knowledge of related subjects and have an opportunity to experience careers.' Based on the results of the pilot application, the high school credit system STEAM program for students and teachers was finally completed in 29 lessons of five types. Through this study, the development and operation of the next-generation STEAM program that can be applied in the high school credit system should be actively developed, and a plan to improve teachers' professionalism so that the high school credit system can be established and operated properly for blended classes triggered by COVID-19. The necessity of design was suggested. This study is expected to be used as basic data for the development and operation of STEAM programs in the high school credit system, which will be fully implemented in 2025.

• Journal of Service Research and Studies
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• v.13 no.3
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• pp.172-189
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• 2023

This study investigated the potential contribution of ChatGPT, a massive language and information model, in the decision-making process of public design policies, focusing on the characteristics inherent to public design. Public design utilizes the principles and approaches of design to address societal issues and aims to improve public services. In order to formulate public design policies and plans, it is essential to base them on extensive data, including the general status of the area, population demographics, infrastructure, resources, safety, existing policies, legal regulations, landscape, spatial conditions, current state of public design, and regional issues. Therefore, public design is a field of design research that encompasses a vast amount of data and language. Considering the rapid advancements in artificial intelligence technology and the significance of public design, this study aims to explore how massive language and information models like ChatGPT can contribute to public design policies. Alongside, we reviewed the concepts and principles of public design, its role in policy development and implementation, and examined the overview and features of ChatGPT, including its application cases and preceding research to determine its utility in the decision-making process of public design policies. The study found that ChatGPT could offer substantial language information during the formulation of public design policies and assist in decision-making. In particular, ChatGPT proved useful in providing various perspectives and swiftly supplying information necessary for policy decisions. Additionally, the trend of utilizing artificial intelligence in government policy development was confirmed through various studies. However, the usage of ChatGPT also unveiled ethical, legal, and personal privacy issues. Notably, ethical dilemmas were raised, along with issues related to bias and fairness. To practically apply ChatGPT in the decision-making process of public design policies, first, it is necessary to enhance the capacities of policy developers and public design experts to a certain extent. Second, it is advisable to create a provisional regulation named 'Ordinance on the Use of AI in Policy' to continuously refine the utilization until legal adjustments are made. Currently, implementing these two strategies is deemed necessary. Consequently, employing massive language and information models like ChatGPT in the public design field, which harbors a vast amount of language, holds substantial value.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.1-17
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• 2023

With the recent rapid development of ICT(Information and Communication Technology) and the popularization of digital devices, the size of the online market continues to grow. As a result, we live in a flood of information. Thus, customers are facing information overload problems that require a lot of time and money to select products. Therefore, a personalized recommender system has become an essential methodology to address such issues. Collaborative Filtering(CF) is the most widely used recommender system. Traditional recommender systems mainly utilize quantitative data such as rating values, resulting in poor recommendation accuracy. Quantitative data cannot fully reflect the user's preference. To solve such a problem, studies that reflect qualitative data, such as review contents, are being actively conducted these days. To quantify user review contents, text mining was used in this study. The general CF consists of the following three steps: user-item matrix generation, Top-N neighborhood group search, and Top-K recommendation list generation. In this study, we propose a recommendation algorithm that applies an extended similarity measure, which utilize quantified review contents in addition to user rating values. After calculating review similarity by applying TF-IDF, Word2Vec, and Doc2Vec techniques to review content, extended similarity is created by combining user rating similarity and quantified review contents. To verify this, we used user ratings and review data from the e-commerce site Amazon's "Health and Personal Care". The proposed recommendation model using extended similarity measure showed superior performance to the traditional recommendation model using only user rating value-based similarity measure. In addition, among the various text mining techniques, the similarity obtained using the TF-IDF technique showed the best performance when used in the neighbor group search and recommendation list generation step.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.17 no.2
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• pp.23-42
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• 2006

This is a study of the digital archives of Culturecontent.com where 'Cultural Archetype Contents' are currently in service. One of the major purposes of our study is to point out problems in the current system and eventually propose improvements to the digital archives. The government launched a four-year project for developing the cultural archetype content sources and establishing its related business with the hope of enhancing the nation's competitiveness. More specifically, the project focuses on the production of source materials of cultural archetype contents in the subjects of Korea's history. tradition, everyday life. arts and general geographical books. In addition, through this project, the government also intends to establish a proper distribution system of digitalized culture contents and to control copyright issues. This paper analyzes the digital archives system that stores the culture content data that have been produced from 2002 to 2005 and evaluates the current system's weaknesses and strengths. The summary of our findings is as follows. First. the digital archives system does not contain a semantic search engine and therefore its full function is 1agged. Second, similar data is not classified into the same categories but into the different ones, thereby confusing and inconveniencing users. Users who want to find source materials could be disappointed by the current distributive system. Our paper suggests a better system of digital archives with text mining technology which consists of five significant intelligent process-keyword searches, summarization, clustering, classification and topic tracking. Our paper endeavors to develop the best technical environment for preserving and using culture contents data. With the new digitalized upgraded settings, users of culture contents data will discover a world of new knowledge. The technology we introduce in this paper will lead to the highest achievable digital intelligence through a new framework.