• 무역상무연구
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• 제21권
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• pp.99-127
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• 2003

Legal sources of fraud rule in documentary letter of credit, which have their origin in Sztejn Case can be traced to various rules or laws of international or domestic level ; URCG, URDG and ISP98 as ICC Rules, and UNCITRAL Convention as an international uniform law, and UCC as a domestic law and U.K. cases. Among them the combination of "material fraud" in UCC ${\S}5-109$ and the detailed list of the types of misconduct in UNCITRAL Convention may provide the best solution or standard in real application of the fraud rule in letter of credit transaction.

• 지능정보연구
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• 제1권2호
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• pp.57-71
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• 1995

This paper presents a rule extraction algorithm RE to acquire explicit rules from trained neural networks. The validity of extracted rules has been confirmed using 6 different data sets. Based on experimental results, we conclude that extracted rules from RE predict more accurately and robustly than neural networks themselves and rules obtained from an inductive learning algorithm do. Rule extraction algorithm for neural networks are important for incorporating knowledge obtained from trained networks into knowledge based systems. In lieu of this, the proposed RE algorithm contributes to the trend toward developing hybrid and versatile knowledge-based system including expert systems and knowledge-based decision su, pp.rt systems.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 춘계정기학술대회
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• pp.253-260
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• 2001

Understanding and adapting to changes of customer behavior is an important aspect for a company to survive in continuously changing environment. The aim of this paper is to develop a methodology which detects changes of customer behavior automatically from customer profiles and sales data at different time snapshots. For this purpose, we first define three types of changes as emerging pattern, unexpected change and the added / perished rule. Then we develop similarity and difference measures for rule matching to detect all types of change. Finally, the degree of change is evaluated to detect significantly changed rules. Our proposed methodology can evaluate degree of changes as well as detect all kinds of change automatically from different time snapshot data. A case study for evaluation and practical business implications for this methodology are also provided.

• 산업경영시스템학회지
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• 제39권3호
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• pp.170-179
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• 2016

In this paper we have tackled the outstanding inventory planning problems over new product launching period in a more holistic manner by addressing first the definition of efficient business rules to effectively control and reduce the inventory risks followed by the rigorous explanations on the implementation guide on suggested inventory planning rules. It is not unusual for many companies in the consumer electronics market to make a great effort to reduce the time to launch a new product because the ability to bring out higher performing products in such a short time period greatly increases the probability for them to remain competitive in the high tech market. Among so many newly developed products, those products with new features and technologies appeal to many potential customers while products which fail to win customers by design and prices rapidly disappear in the market. To adapt to this business environment, those companies have been trying to find the answer to minimize the inventory of old products so they can move to next generation products quickly with less obsolete material. In the experimental implementation of our rule-based inventory planning, Company 'S' reduced the inventory cost for the outgoing products as low as 49% of its peak level of its preceding product version in just 5 month after the adoption of rule-based inventory planning process and system. This paper concluded the subject with a suggestion that the best performance of rule-based inventory planning is guaranteed not from one-time campaign of process improvement along with system development but the decision maker's continuing support and attention even without seeing any upcoming business crisis.

• 경영정보학연구
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• 제12권1호
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• pp.131-144
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• 2010

급변하는 경영 환경 속에서 기업이 생존하기 위해서는 환경변화에 대하여 효과적이고 신속하게 대응하는 것이 매우 중요하다. 환경변화에 적절하게 대응하기 위한 접근방안의 하나로서 기업은 다양한 비즈니스 룰과 관련지식을 개발하여 활용하여 왔으며 정보시스템을 통해 이를 구현하고자 노력하고 있다. 하지만 기업이 사용하고 있는 기존의 정보시스템 대부분은 기업이 보유하고 있는 비즈니스 룰을 적시에 적절하게 활용하거나 체계적으로 관리하는데 있어서 한계를 지니고 있다. 본 연구에서는 이러한 한계를 극복하기 위한 솔루션으로서 비즈니스 룰 엔진(BRE: Business Rule Engine)의 도입을 제안하며 "W" 투자증권의 실제 구현사례를 통해 조직민첩성에 미치는 효과를 분석한다.

• 한국컴퓨터정보학회논문지
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• 제10권4호
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• pp.345-354
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• 2005

본 논문에서는 웹 기반 분산 기업 시스템 사이의 협력을 분석하여 적기 협력의 필요성을 파악하였고 이를 기업 사이의 계약 관점에서 기술하였다. 그리고 이러한 기업 시스템 사이의 적기 협력을 지원할 수 있는 ECA(event condition action) 규칙 기반 방안과 이 방안을 지원하는 능동 기능 컴포넌트(AFC)를 제안한다. 제안된 방안은 고수준의 ECA 규칙 프로그래밍과 사건 기반의 즉시 처리 방식을 지원하므로 시스템 관리자나 프로그래머가 응용 로직과는 별도로 적기 협력을 쉽게 구축하고 유지 보수할 수 있다. 제안하는 능동 기능 컴포넌트는 기업의 방화벽을 통해서도 적용될 수 있도록 HTTP 프로토콜을 사용하였고, 실용성을 위하여 상업용 DBMS의 기본 트리거를 이용하여 구현하였다.

• 지능정보연구
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• 제12권3호
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• pp.95-110
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• 2006

지능형 에이전트와 규칙기반 시스템을 이용해 보다 지능적인 웹 환경을 구축하고자 하는 노력이 시맨틱 웹의 발전과 함께 증가하고 있다. 이러한 에이전트와 규칙기반 시스템에 필요한 규칙들을 이미 많은 지식들이 산재해 있는 웹으로부터 습득할 수 있다면 보다 효율적으로 시스템을 구축하는 것이 가능하며, 이러한 응용시스템의 확장은 시맨틱 웹의 발전을 더욱 가속화하는 계기가 될 수 있을 것이다. XRML 방법론은 웹으로부터 규칙을 습득하기 위한 단계적 방법을 제시하고 있으며, 온톨로지를 이용함으로써 규칙의 구성요소들을 자동으로 추출할 수 있도록 지원한다. 그러나 추출된 규칙구성요소들을 조합하여 완전한 규칙을 만드는 과정이 규칙관리자의 수작업에 의존하고 있다. 본 연구는 온톨로지와 그래프 탐색을 사용함으로써 이 과정을 자동화하고자 하는 연구이다. 온톨로지에 있는 규칙의 일반적 패턴을 기반으로 하여 그래프 탐색을 이용해 규칙구성요소들을 조합함으로써 웹 페이지로부터 자동으로 규칙을 추출할 수 있다.

• 산업경영시스템학회지
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• 제40권2호
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• pp.137-144
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• 2017

The business process of global third party logistics company is defined as a network of logistics activities which involves the products that are manufactured in the developing countries, such as Vietnam, China and so on, and delivered to North or South American countries via intermediate stopover sites. The third party logistics company usually uses proprietary logistics information system to support the related logistics activities. However, each consignor sometimes may require different business process based on the customer type or characteristics of their products. Therefore, the third party logistics company need to modify their business process to reflect customer's requirements, resulting in the modification of logistic information systems and additional costs. Therefore, a flexible mechanism is required to efficiently support the various types of requirements by the owners of the products. In this paper, first, we figured out various business rules related to third party global logistics activities. Second, we grouped the identified business rules into business processes, objects, relations, dependency, policy, representations, execution, and resources and further into precondition, postcondition, and invariant based on checking point in time. Furthermore, the categorized rules are classified into inter-activity and intra-activity rules based on the execution range. Third, we proposed a rule syntax to describe the defined rules into scripts which are understood by user and information system together. When each activity is executed, the rule manager checks whether there are rules related with the activity execution. Finally, we developed a prototype rule management system to show the feasibility of our proposed methodology and to validate it with an example.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2005년도 추계학술대회 학술발표 논문집 제15권 제2호
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• pp.271-275
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• 2005

In this research, we propose a Unified Fuzzy rule-based knowledge Inference Systems UFIS) to help the expert in cosmetic brand detection. Users' preferred cosmetic product detection is very important in the level of CRM. To this Purpose, many corporations trying to develop an efficient data mining tool. In this study, we develop a prototype fuzzy rule detection and inference system. The framework used in this development is mainly based on two different mechanisms such as fuzzy rule extraction and RDB (Relational DB)-based fuzzy rule inference. First, fuzzy clustering and fuzzy rule extraction deal with the presence of the knowledge in data base and its value is presented with a value between $0\∼1$. Second, RDB and SQL(Structured Query Language)-based fuzzy rule inference mechanism provide more flexibility in knowledge management than conventional non-fuzzy value-based KMS(Knowledge Management Systems)

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2003년도 춘계공동학술대회
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• pp.884-888
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• 2003

In this paper, we introduce the hybrid data mining mechanism based in association rule and fuzzy neural networks (FNN). Most of data mining mechanisms are depended in the association rule extraction algorithm. However, the basic association rule-based data mining has not the learning ability. In addition, sequential patterns of association rules could not represent the complicate fuzzy logic. To resolve these problems, we suggest the hybrid mechanism using association rule-based data mining, and fuzzy neural networks. Our hybrid data mining mechanism was consisted of four phases. First, we used general association rule mining mechanism to develop the initial rule-base. Then, in the second phase, we used the fuzzy neural networks to learn the past historical patterns embedded in the database. Third, fuzzy rule extraction algorithm was used to extract the implicit knowledge from the FNN. Fourth, we combine the association knowledge base and fuzzy rules. Our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic.