• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.227-240
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• 2011

New concepts and ideas often result from extensive recombination of existing concepts or ideas. Both researchers and developers build on existing concepts and ideas in published papers or registered patents to develop new theories and technologies that in turn serve as a basis for further development. As the importance of patent increases, so does that of patent analysis. Patent analysis is largely divided into network-based and keyword-based analyses. The former lacks its ability to analyze information technology in details while the letter is unable to identify the relationship between such technologies. In order to overcome the limitations of network-based and keyword-based analyses, this study, which blends those two methods, suggests the keyword network based analysis methodology. In this study, we collected significant technology information in each patent that is related to Light Emitting Diode (LED) through text mining, built a keyword network, and then executed a community network analysis on the collected data. The results of analysis are as the following. First, the patent keyword network indicated very low density and exceptionally high clustering coefficient. Technically, density is obtained by dividing the number of ties in a network by the number of all possible ties. The value ranges between 0 and 1, with higher values indicating denser networks and lower values indicating sparser networks. In real-world networks, the density varies depending on the size of a network; increasing the size of a network generally leads to a decrease in the density. The clustering coefficient is a network-level measure that illustrates the tendency of nodes to cluster in densely interconnected modules. This measure is to show the small-world property in which a network can be highly clustered even though it has a small average distance between nodes in spite of the large number of nodes. Therefore, high density in patent keyword network means that nodes in the patent keyword network are connected sporadically, and high clustering coefficient shows that nodes in the network are closely connected one another. Second, the cumulative degree distribution of the patent keyword network, as any other knowledge network like citation network or collaboration network, followed a clear power-law distribution. A well-known mechanism of this pattern is the preferential attachment mechanism, whereby a node with more links is likely to attain further new links in the evolution of the corresponding network. Unlike general normal distributions, the power-law distribution does not have a representative scale. This means that one cannot pick a representative or an average because there is always a considerable probability of finding much larger values. Networks with power-law distributions are therefore often referred to as scale-free networks. The presence of heavy-tailed scale-free distribution represents the fundamental signature of an emergent collective behavior of the actors who contribute to forming the network. In our context, the more frequently a patent keyword is used, the more often it is selected by researchers and is associated with other keywords or concepts to constitute and convey new patents or technologies. The evidence of power-law distribution implies that the preferential attachment mechanism suggests the origin of heavy-tailed distributions in a wide range of growing patent keyword network. Third, we found that among keywords that flew into a particular field, the vast majority of keywords with new links join existing keywords in the associated community in forming the concept of a new patent. This finding resulted in the same outcomes for both the short-term period (4-year) and long-term period (10-year) analyses. Furthermore, using the keyword combination information that was derived from the methodology suggested by our study enables one to forecast which concepts combine to form a new patent dimension and refer to those concepts when developing a new patent.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• The Korean Journal of Archival Studies
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• no.8
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• pp.137-180
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• 2003

The term 'Business Archives' is not familiar with us in our society. Some cases can be found that materials are collected for publishing the history of a firm on commemoration of some decades of its foundation. However, the appropriate management of these collected materials doesn't seem to be followed in most of companies. The Records and archives management is inevitable in order to maximize the utility of Information and knowledge in the business world. The interest in records management has been grown, especially in the fields of business management and information technology. However, the importance of business archives hasn't been conceived yet. And also no attention has been paid to the business archives as social resources and the responsibility of the society as a whole for their preservation. The company archives doesn't have a long history in Germany although the archives of the nation, the aristocracy, communes and churches have a long tradition. However the company archives of Krupps which was established in 1905, is regarded as the first business archives in the world, It means that Germany has taken a key role to lead the culture of business archives. This paper focuses on the process of the establishment of business archives in Germany and its characteristics. The business archives in Germany can be categorized in three types: company archives, regional business archives and branch archives. It must be noted here that each type of these was generated in the context of the accumulation of the social resources and its effective use. A company archives is established by an individual company for the preservation of and use of the archives that originated in the company. The holdings in the company archives can be used as materials for decision making of policies, reporting, advertising, training of employees etc. They function not only as sources inside the company, but also as raw sources for the scholars, contributing to the study of the social-economic history. Some archives of German companies are known as a center of research. A regional business archives manages materials which originated m commerce chambers, associations and companies in a certain region. There are 6 regional business archives in Germany. They collect business archives which aren't kept in a proper way or are under pressure of damage in the region for which they are responsible. They are also open to the public offering the sources for the study of economic history, social history like company archives, so that they also play a central role as a research center. Branch business archives appeared relatively late in Germany. The first one is established in Bochum in 1969. Its general duties and goals are almost similar with ones of other two types of archives. It has differences in two aspects. One is that the responsibility of the branch business archives covers all the country, while regional business archives collects archives in a particular region. The other is that a branch business archives collects materials from a single industry. For example, the holdings of Bochum archives are related with the mining industry. The mining industry-specialized Bochum archives is run as an organization in combination with a museum, which is called as German mine museum, so that it plays a role as a cultural center with the functions of exhibition and research. The three types of German business archives have their own functions but they are also closely related each other under the German Association of Business Archivists. They are sharing aims to preserve primary materials with historical values in the field of economy and also contribute to keeping the archives as a social resources by having feed back with the public, which leads the archives to be a center of information and research. The German case shows that business archives in a society should be preserved not only for the interest of the companies, but also for the utilities of social resources. It also shows us how business archives could be preserved as a social resource. It is expected that some studies which approach more deeply on this topic will be followed based on the considerations from the German case.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.1-23
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• 2019

Big data refers to data that is difficult to store, manage, and analyze by existing software. As the lifestyle changes of consumers increase the size and types of needs that consumers desire, they are investing a lot of time and money to understand the needs of consumers. Companies in various industries utilize Big Data to improve their products and services to meet their needs, analyze unstructured data, and respond to real-time responses to products and services. The financial industry operates a decision support system that uses financial data to develop financial products and manage customer risks. The use of big data by financial institutions can effectively create added value of the value chain, and it is possible to develop a more advanced customer relationship management strategy. Financial institutions can utilize the purchase data and unstructured data generated by the credit card, and it becomes possible to confirm and satisfy the customer's desire. CRM has a granular process that can be measured in real time as it grows with information knowledge systems. With the development of information service and CRM, the platform has change and it has become possible to meet consumer needs in various environments. Recently, as the needs of consumers have diversified, more companies are providing systematic marketing services using data mining and advanced CRM (Customer Relationship Management) techniques. KB Kookmin Card, which started as a credit card business in 1980, introduced early stabilization of processes and computer systems, and actively participated in introducing new technologies and systems. In 2011, the bank and credit card companies separated, leading the 'Hye-dam Card' and 'One Card' markets, which were deviated from the existing concept. In 2017, the total use of domestic credit cards and check cards grew by 5.6% year-on-year to 886 trillion won. In 2018, we received a long-term rating of AA + as a result of our credit card evaluation. We confirmed that our credit rating was at the top of the list through effective marketing strategies and services. At present, Kookmin Card emphasizes strategies to meet the individual needs of customers and to maximize the lifetime value of consumers by utilizing payment data of customers. KB Kookmin Card combines internal and external big data and conducts marketing in real time or builds a system for monitoring. KB Kookmin Card has built a marketing system that detects realtime behavior using big data such as visiting the homepage and purchasing history by using the customer card information. It is designed to enable customers to capture action events in real time and execute marketing by utilizing the stores, locations, amounts, usage pattern, etc. of the card transactions. We have created more than 280 different scenarios based on the customer's life cycle and are conducting marketing plans to accommodate various customer groups in real time. We operate a smart offering system, which is a highly efficient marketing management system that detects customers' card usage, customer behavior, and location information in real time, and provides further refinement services by combining with various apps. This study aims to identify the traditional CRM to the current CRM strategy through the process of changing the CRM strategy. Finally, I will confirm the current CRM strategy through KB Kookmin card's big data utilization strategy and marketing activities and propose a marketing plan for KB Kookmin card's future CRM strategy. KB Kookmin Card should invest in securing ICT technology and human resources, which are becoming more sophisticated for the success and continuous growth of smart offering system. It is necessary to establish a strategy for securing profit from a long-term perspective and systematically proceed. Especially, in the current situation where privacy violation and personal information leakage issues are being addressed, efforts should be made to induce customers' recognition of marketing using customer information and to form corporate image emphasizing security.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.57-84
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• 2023

With the development of information and communications technology (ICT) and big data technology, anyone can easily obtain and utilize vast amounts of data through the Internet. Therefore, the capability of selecting high-quality data from a large amount of information is becoming more important than the capability of just collecting them. This trend continues in academia; literature reviews, such as systematic and non-systematic reviews, have been conducted in various research fields to construct a healthy knowledge structure by selecting high-quality research from accumulated research materials. Meanwhile, after the COVID-19 pandemic, remote healthcare services, which have not been agreed upon, are allowed to a limited extent, and new healthcare services such as health recommender systems (HRS) equipped with artificial intelligence (AI) and big data technologies are in the spotlight. Although, in practice, HRS are considered one of the most important technologies to lead the future healthcare industry, literature review on HRS is relatively rare compared to other fields. In addition, although HRS are fields of convergence with a strong interdisciplinary nature, prior literature review studies have mainly applied either systematic or non-systematic review methods; hence, there are limitations in analyzing interactions or dynamic relationships with other research fields. Therefore, in this study, the overall network structure of HRS and surrounding research fields were identified using citation network analysis (CNA). Additionally, in this process, in order to address the problem that the latest papers are underestimated in their citation relationships, the GraphSAGE algorithm was applied. As a result, this study identified 'recommender system', 'wireless & IoT', 'computer vision', and 'text mining' as increasingly important research fields related to HRS research, and confirmed that 'personalization' and 'privacy' are emerging issues in HRS research. The study findings would provide both academic and practical insights into identifying the structure of the HRS research community, examining related research trends, and designing future HRS research directions.

• 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.

• Proceedings of the International Microelectronics And Packaging Society Conference
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• 2001.11a
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• pp.37-39
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• 2001

21세기의 벽두부터 국내외적으로 활발히 논의되고 있는 나노기술에 대한 정의를 생각해보는 것으로부터 우리가 나아갈 방향을 살펴보고자 한다. 나노기술이란, 원자 하나 하나 혹은 분자단위의 조작을 통해 1~100nm정도의 범위 안에서 근본적으로 새로운 물질이나 구조체를 만들어 내는 기술을 말한다. 즉 앞으로 우리는 경험해 보지 못한 새로운 현상에 대한 이해를 할 수 있어야 하고, 새로운 물질 자체를 다룰 수 있는 방법이 우리가 해야 할 구체적인 일이 될 것이란 말이 된다. 뿐만 아니라 나노기술은 종래의 정보.통신.전자 분야에서 주로 추구하던 마이크로화와 달리 재료, 기계, 전자, 의학, 약학, 에너지, 환경, 화학, 생물학, 농학, 정보, 보안기술 등 과학기술 분야 전반을 위시하여 사회분야가지 새로운 인식과 철학적인 이해가 필요하게 되었다. 21세기를 맞은 인류가 나아갈 방향을 나노세계에 대한 도전으로 보아야 하며, 과학기술의 새로운 틀을 제공할 것 임에 틀림 없다. 그러나, 이와 같은 나노기술의 출발점을 살펴보면 VLSI기술로 통칭할 수 있는 마이크로전자소자 기술이란 점이다. 국내의 VLSI기술은 메모리기술이라고 해도 과언이 아닐 것이다. 문제는 종래의 메모리기술은 대규모 투자와 집중적인 인력양성을 통해서 세계 최고 수준에 도달 할 수 있었다. 그러나 여기까지 오는 동안 사식 우리는 선진국의 뒷꽁무니를 혼신의 힘을 다해 뒤쫓아 온 결과라고 보아도 틀리지 않는다. 즉, 앞선자를 보고 뒤쫓는 사람은 갈방향과 목표가 분명하므로 최선을 다하면 따라 잡을 수 있다. 그런데 나노기술은 앞선 사람이 없다는 점이 큰 차이이다 따라서 뒷껑무니를 쫓아가는 습성을 가지고는 개척해 나갈 수 없다는 점을 깨닫지 않으면 안된다. 그런 점에서 이 시간 나노기술의 국내외 현황을 살펴보고 우리가 어떻게 할 것인가를 생각해 보는데 의미가 있을 것이다.하여 분석한 결과 기존의 제한된 RICH-DP는 실시간 서비스에 대한 처리율이 낮아지며 서비스 시간이 보장되지 못했다. 따라서 실시간 서비스에 대한 새로운 제안된 기법을 제안하고 성능 평가한 결과 기존의 RICH-DP보다 성능이 향상됨을 확인 할 수 있었다.(actual world)에서 가상 관성 세계(possible inertia would)로 변화시켜서, 완수동사의 종결점(ending point)을 현실세계에서 가상의 미래 세계로 움직이는 역할을 한다. 결과적으로, IMP는 완수동사의 닫힌 완료 관점을 현실세계에서는 열린 미완료 관점으로 변환시키되, 가상 관성 세계에서는 그대로 닫힌 관점으로 유지 시키는 효과를 가진다. 한국어와 영어의 관점 변환 구문의 차이는 각 언어의 지속부사구의 어휘 목록의 전제(presupposition)의 차이로 설명된다. 본 논문은 영어의 지속부사구는 논항의 하위간격This paper will describe the application based on this approach developed by the authors in the FLEX EXPRIT IV n$^{\circ}$EP29158 in the Work-package "Knowledge Extraction & Data mining"where the information captured from digital newspapers is extracted and reused in tourist information context.terpolation performance of CNN was relatively better than NN.콩과 자연 콩이 성분 분석에서 차이를 나타내지 않았다는 점, 네 번째. 쥐를 통한 다양섭취 실험에서 아무런 이상 반응이 없었다는 점등의 결과를 기준으로 알레르기에 대한 개별 검사 없이 안전한

• Journal of The Korean Association of Information Education
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• v.9 no.3
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• pp.453-462
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• 2005

Recently, due to technical improvements of storage devices and networks, the amount of data increase rapidly. In addition, it is required to find the knowledge embedded in a data stream as fast as possible. Huge data in a data stream are created continuously and changed fast. Various algorithms for finding frequent itemsets in a data stream are actively proposed. Current researches do not offer appropriate method to find frequent itemsets in which flow of time is reflected but provide only frequent items using total aggregation values. In this paper we proposes a novel algorithm for finding the relative frequent itemsets according to the time in a data stream. We also propose the method to save frequent items and sub-frequent items in order to take limited memory into account and the method to update time variant frequent items. The performance of the proposed method is analyzed through a series of experiments. The proposed method can search both frequent itemsets and relative frequent itemsets only using the action patterns of the students at each time slot. Thus, our method can enhance the effectiveness of learning and make the best plan for individual learning.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.335-345
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• 1999

오늘날 날로 증가하는 물류비는 개별 기업은 물론 국가 전체의 수출 경쟁력을 약화시키는 주요 원인으로 지적되고 있다. 그러나 그동안 우리나라에서는 물류비 절감을 위한 종합적이고 체계적인 대책이 이루어지지 못하였다. 특히 본 논문의 연구대상인 육상물류의 경우 그 비중이 전체 화물 운송의 60% 이상을 차지함에도 불구하고 심각한 교통체증 및 물류기반 시설의 미비 등으로 인하여 물류비가 계속 증가하는 양상을 보여 왔다. 따라서 본 논문에서는 우리나라 육상물류시스템이 안고 있는 문제점의 해결을 위한 방안들 중의 하나로 정보기술의 활용에 관한 내용을 다루고 있다. 즉 영세한 기업들도 누구나 손쉽게 이용할 수 있도록 인터넷을 이용한 육상물류중개시스템의 개발에 관한 내용을 소개하고 있다. 육상물류중개시스템은 복합화물주선업체인 (주) 대형물류와 함께 개발한 시스템으로 인터넷을 통하여 화주의 화물 운송의뢰를 접수받아 이를 여러 운송업체에게 제공해 주는 역할을 수행하게 된다. 특히 육상물류중개시스템은 화물의 운송과 관련하여 발생하는 다양한 정보들을 데이터베이스에 저장하여 두었다가 세관을 비롯한 터미널에 대한 각종 신고업무에 이용할 수 있으며, 이밖에도 교통정보 및 화물 위치정보 등 다양한 서비스를 제공해 줄 수 있다. 따라서 운송업체의 공차율을 줄이고 화주에게는 자신의 화물에 대한 정보를 실 시간으로 전달해 줄 수 있다는 장점이 있다. 또한 이러한 육상물류중개시스템은 현재 개발중인 통합데이터베이스를 기반으로한 항만물류원스톱서비스 시스템과 연계되어 차후에는 물류원스톱시스템으로 발전할 수 있을 것이다.용되어져 왔다. 그러나 MCRDR 이론이 적용된 전문가시스템들의 경우 MCRDR이론을 기본으로한 개발 툴로서 개발된 시스템들이 아니고 해당분야에서 MCRDR이론을 적용한 엔진을 직접 설계 구현하여 온 것이 사실이다. KEE(Knowledge Engineer for Experts) 시스템은 최근 개발된 MCRDR기반 전문가시스템 개발 툴로서 본 논문에서는 이러한 분야별 전문가시스템 개발을 지양하고 MCRDR 이론을 기반으로 한 범용성 있는 전문가시스템 개발 툴의 개발에 관한 연구를 소개한다.-based Data Mining Architecture를 제시하였다. 본 연구의 의의로는 데이터 마이닝을 통한 귀납적 지식생성에 있어 귀납적 오류의 발생을 도메인 지식을 통해 설명가능 함을 보임으로 검증하고 아울러 이러한 설명을 통해 연역적으로 새로운 가설지식을 생성시켜 이를 가설검증방식으로 검증함으로써 귀납적 접근과 연역적 접근의 통합 데이터 마이닝 접근을 제시하였다는데 있다.osed algorithm are faster and lower than the existing LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.768-770
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• 2015

We performed integration and standardization of the omics data related agriculture. To do this, we requires progressed computational methods and bioinformatics infrastructures for integration, standardization, mining, and analysis. It makes easier biological knowledge to find. we potentialize registration a row and processed data in NABIC (National Agricultural Biotechnology Information Center) and its processed analysis results were offered related researchers. And we also provided various analysis pipelines, NGS analysis (Reference assembly, RNA-seq), GWAS, Microbial community analysis. In addition, the our system was carried out based on the design and build the quality assurance in management omics information system and constructed the infrastructure for utilization of omics analyze system. We carried out major improvement quality of omics information system. First is Improvement quality of registration category for omics based information. Second is data processing and development platform for web UI about related omics data. Third is development of proprietary management information for omics registration database. Forth is management and development of the statistics module producers about omics data. Last is Improvement the standard upload/ download module for Large omics Registration information.