• Journal of Korea Multimedia Society
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• v.7 no.2
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• pp.145-155
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• 2004

It is very important for Internet searching systems to have user-friendly and rapid searching functions at the managers'point of view. The former finds optimized input parameters to support the various searching requirements of user. The latter has fast searching results which are effectively normalized to various input parameters having different attributes. In this paper we basically focus on optimized database construction not only to have searching functions with multiple attributes to support maximal various input requirements of the user but also to have more rapid searching functions. For this research, we suggest a modified association algorithm that takes into consideration to the support and confidence that is the criteria of the association mining rule in order to reflect the searching characteristics of internet shopping malls. We also propose the model management systems for rapid searching functions. The following results are from a processed simulation: the more the number of searching transactions of the users increase, the less the total relative average searching time becomes.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.27-42
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• 2020

Traditional companies with offline stores were unable to secure large display space due to the problems of cost. This limitation inevitably allowed limited kinds of products to be displayed on the shelves, which resulted in consumers being deprived of the opportunity to experience various items. Taking advantage of the virtual space called the Internet, online shopping goes beyond the limits of limitations in physical space of offline shopping and is now able to display numerous products on web pages that can satisfy consumers with a variety of needs. Paradoxically, however, this can also cause consumers to experience the difficulty of comparing and evaluating too many alternatives in their purchase decision-making process. As an effort to address this side effect, various kinds of consumer's purchase decision support systems have been studied, such as keyword-based item search service and recommender systems. These systems can reduce search time for items, prevent consumer from leaving while browsing, and contribute to the seller's increased sales. Among those systems, recommender systems based on association rule mining techniques can effectively detect interrelated products from transaction data such as orders. The association between products obtained by statistical analysis provides clues to predicting how interested consumers will be in another product. However, since its algorithm is based on the number of transactions, products not sold enough so far in the early days of launch may not be included in the list of recommendations even though they are highly likely to be sold. Such missing items may not have sufficient opportunities to be exposed to consumers to record sufficient sales, and then fall into a vicious cycle of a vicious cycle of declining sales and omission in the recommendation list. This situation is an inevitable outcome in situations in which recommendations are made based on past transaction histories, rather than on determining potential future sales possibilities. This study started with the idea that reflecting the means by which this potential possibility can be identified indirectly would help to select highly recommended products. In the light of the fact that the attributes of a product affect the consumer's purchasing decisions, this study was conducted to reflect them in the recommender systems. In other words, consumers who visit a product page have shown interest in the attributes of the product and would be also interested in other products with the same attributes. On such assumption, based on these attributes, the recommender system can select recommended products that can show a higher acceptance rate. Given that a category is one of the main attributes of a product, it can be a good indicator of not only direct associations between two items but also potential associations that have yet to be revealed. Based on this idea, the study devised a recommender system that reflects not only associations between products but also categories. Through regression analysis, two kinds of associations were combined to form a model that could predict the hit rate of recommendation. To evaluate the performance of the proposed model, another regression model was also developed based only on associations between products. Comparative experiments were designed to be similar to the environment in which products are actually recommended in online shopping malls. First, the association rules for all possible combinations of antecedent and consequent items were generated from the order data. Then, hit rates for each of the associated rules were predicted from the support and confidence that are calculated by each of the models. The comparative experiments using order data collected from an online shopping mall show that the recommendation accuracy can be improved by further reflecting not only the association between products but also categories in the recommendation of related products. The proposed model showed a 2 to 3 percent improvement in hit rates compared to the existing model. From a practical point of view, it is expected to have a positive effect on improving consumers' purchasing satisfaction and increasing sellers' sales.

• Journal of the Korea Society of Computer and Information
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• v.14 no.6
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• pp.125-134
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• 2009

A biological data acquisition based on web has emerged as a powerful tool for allowing scientists to interactively view entries form different databases, and to navigate from one database to another molecular-biology database links. In this paper, the biological conceptual model is constructed hybrid biological data model to represent interesting entities in the data sources to applying navigation rule property for each biological data source based on four biological data integrating layers to control biological data. When some user's requests for application service are occurred, we can get the data from database and data source via web service. In this paper, we propose a query processing model and execution structure based on integrating data layers that can search information on biological data sources.

• The Transactions of the Korea Information Processing Society
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• v.7 no.8S
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• pp.2688-2699
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• 2000

본 논문에서는 시간속성과 능동특성을 지원하는 관리객체의 동적특성 표현언어 BDL(Behaviour Description Language)을 이용하여 시스템 망관리 모델의 관리기능을 정형적으로 표현하였다. 그리고 BDL로 표현된 관리기능을 CORBA IDL로 변환하는 BDL＿to＿IDL 컴파일러를 설계·구현하였다. 특히, 망 관리 정보베이스에 저장되어있는 관리객체를 안전하게 보호하기 위해 ITU-T 권고안에 정의된 강제적 접근제어 모델과 역할기반 접근제어 모델을 상호연동한 접근제어모델을 정의하였다. 또한, 관리속성값을 제어하는 관리연산을 연관된 유형별로 묶어 역할로 정의하고 관리자와 관리객체에 인가등급과 보안등급을 부여하여 역할배정규칙과 제약조건에 따라 관리정보의 접근을 제어함으로써 보다 무결성을 보장받도록 하였다.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10c
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• pp.736-738
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• 1999

본 논문에서는 ITU-T 권고안에 정의된 장제적 접근제어 모델과 역할기반 접근제어 모델을 상호연동한 모델의 관리객체 상호관계구조를 정의하였다. 또한 관리속성값을 제어하는 관리연산을 연관된 유형별로 묶어 역할로 정의하였으며 관리자와 관리객체에 보안등급을 부여함으로써 무결성을 보장하고 관리자 관리객체 사이에 역할을 배정하므로써 실생활에 적용될 수 있는 접근제어 모델을 제시하였다. 그리고 역할배정규칙과 제약조건을 기반으로 하여 'rule' 관리객체 클래스의 접근제어 결정함수와 접근제어 집행함수의 동작과정을 동적특성 기술언어를 체계적이고 정형적으로 기술하였다.

• Journal of Korean Society of societal Security
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• v.4 no.2
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• pp.29-40
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• 2011

The objective of this paper is to develop an intelligent decision support system that is able to advise disaster countermeasures and degree of incidents on the basis of the collected and analyzed signs of disasters. The concepts derived from ontology, text mining and case-based reasoning are adapted to design the system. The functions of this system include term-document matrix, frequency normalization, confidency, association rules, and criteria for judgment. The collected qualitative data from signs of new incidents are processed by those functions and are finally compared and reasoned to past similar disaster cases. The system provides the varying degrees of how dangerous the new signs of disasters are and the few countermeasures to the disaster for the manager of disaster management. The system will be helpful for the decision-maker to make a judgment about how much dangerous the signs of disaster are and to carry out specific kinds of countermeasures on the disaster in advance. As a result, the disaster will be prevented.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.1
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• pp.12-23
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• 2003

The expansion of internet and the development of communication technology have brought about an explosive increasement of data. Further progress has led to the increasing demand for efficient and effective data analysis tools. According to this demand, data mining techniques have been developed to find out knowledge from a huge amounts of raw data. This paper suggests a generalization based classification method which explores rules from real world data appearing repeatedly. Also, it analyzed the relation between weather data and forest fire, and efficiently predicted through it as a prediction model by applying the suggested generalization based classification method to forest fire data. Additionally, the proposed method can be utilized variously in the important field of real life like the analysis and prediction on natural disaster occurring repeatedly, the prediction of energy demand and so forth.

• Journal of Digital Convergence
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• v.3 no.1
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• pp.75-88
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• 2005

최근 우리는 인접학문 간 그리고 학계와 산업계간의 연구협조가 점차 증가하고 있음을 보아오고 있다. 이러한 현상은 특히 학술저널 간 지식의존성을 촉진하는 계기를 제공하고 있다고 할 수 있다. 본 논문의 목적은 관련저널 간 지식상호 의존성을 규명하고 저널지식의 구조화를 위하여 연관성 (association), 군집화, 링크분석 등 데이터마이닝 기법을 적용하는 방법론을 제시하는 것이다. 제시된 방법을 통하여 기대되는 점들은 1) 논문의 기본 속성인 키워드, 저자, 그리고 인용데이터를 통합하는 규칙 집합을 통하여 논문지식검색기능의 향상, 2) 키워드를 기반으로 관련 저널 간 그리고 저널내부의 군집분석으로 지식동향 파악, 3) Kleinberg (1999)의 권위와 허브 개념을 인용데이터 분석에 활용하여 기존의 양적 평가 기준인 영향력지수 (impact factor)의 문제점을 보완하며, 4) 특정 논문이나 저널의 지식파급과 관련한 영향력을 산출하는 잠재적 지식파급 지수를 제안하는 것이다.

• KDI Journal of Economic Policy
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• v.17 no.4
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• pp.3-61
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• 1995

기업활동의 범세계화(汎世界化), WTO 출범 등으로 세계경제(世界經濟)의 통합(統合)과 무국경화(無國境化)가 진행되고 있다. 이에 따라 자본 고급인력 등 국제이동성(國際移動性)이 높은 자원들이 유동화(流動化)할 것이고, 그 결과 토지, 사회간접자본, 미숙련 노동력, '경기규칙', 사회 전반적인 과학기술수준, 문화 등 국제이동성(國際移動性)이 낮은 광의(廣義)의 생산요소(生産要素)들이 한 나라의 경제적 특성을 결정하고 경제 기반을 이룰 것이다. 무국경시대(無國境時代)에는 자원배분뿐 아니라 자원유치(資源幽致)가 한 나라의 경제성과에 큰 영향을 미칠 것이며, 따라서 자원유치가 경제운영의 중요한 과제로 등장할 것으로 예상된다. 자원의 국제적(國際的) 유동화(流動化)는 국제이동성(國際移動性)이 높은 생산요소들이 국제이동성이 낮은 생산요소들을 찾아 경제활동의 근거지를 선택하는 것이므로, 무국경시대(無國境時代)에는 저이동성(低移動性) 생산요소(生産要素)들의 양적(量的) 확충(擴充)및 질적(質的) 수준(水準) 제고(提高)를 통한 자원유치(資源誘致)의 가능성이 커지며, 따라서 일부 첨단기술산업의 육성보다는 전반적인 과학기술(科學技術) 수준(水準) 제고(提高)가, 소수의 고급인력 확보보다는 다수(多數) 미숙련(未熟練) 인력(人力)의 질적(質的) 수준(水準) 제고(提高)가 경제성과를 높이는 데 상대적으로 더 중요해진다. 또한 경제적(經濟的) 무국경화(無國境化)는 국적에 관한 속인주의(屬人主義)의 퇴조와 속지주의(屬地主義)의 보편화, 한 나라 국경 안에 상이한 특성을 가진 지방경제(地方經濟)들의 부상, 국내 산업들 사이의 산업연관관계(産業聯關關係) 약화(弱化) 등의 변화를 수반할 것으로 예상된다. 이같은 변화로 개방주의(開放主義) 및 무차별주의(無差別主義)의 확대(擴大)가 불가피하게 되고 특정 산업에 대한 정부지원 및 보호의 근거가 약화되는 반면, 자원배분의 참고단위로서 개별(個別) 경제주체(經濟主體)들의 중요성이 높아지며 그만큼 시장경쟁을 지배하는 '경기규칙(鏡技規則)'의 올바른 정립이 중요해진다. 그러므로 정부는 자원배분에 대한 개업을 축소하고, 저이동성(低移動性) 생산요소(生産要素)들의 양적 질적 수준 제고, 특히 '경기규칙(競技規則)'의 공정성(公正性) 및 투명성(透明性)을 높여야 한다. 즉 정부가 폐쇄성 높은 경제의 지배인으로부터 개방(開放)된 시장경제(市場經濟)의 후견인으로 바뀌어야 한다. 이것이, 시장질서(市場秩序)가 우리를 먹여 살리는 손이라는 인식이나 국제분업(國際分業)이 살 길이라는 확신이 부족한 우리 사회에 무국경시대(無國境時代)가 던지는 어려운 도전(挑戰)이다.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.111-126
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• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.