• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.21
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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.

• Journal of Intelligence and Information Systems
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• v.1 no.2
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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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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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.

• Information Systems Review
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• v.12 no.1
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• pp.131-144
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• 2010

To survive in the rapidly changing business environment, it is very important for companies to respond to the changing environment effectively as well as agilely. As an approach to appropriately respond to the changing environment, companies have developed and exploited various business rules and related knowledge and attempted to implement them through information systems. However, most of legacy information systems used in companies have suffered from the limitations that do not properly utilize and systematically organize the business rules. This study proposes an introduction of BRE (business rule engine) as a solution to cope with the limitations and explores its effect on organizational agility based the case analysis of "W" Investment and securities Co., Ltd.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.345-354
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• 2005

In this paper collaboration of web-based distributed business systems is analyzed and the need of timely collaboration is derived and described in terms of inter-organizational contracts. A method of event-condition-action (ECA) rule based timely collaboration to meet the need and an active functionality component (AFC) to provide the method are proposed. The proposed method supports high level rule programming and event-based immediate processing so that system administrators and programmers can easily maintain the timely collaboration independently to the application logic. The proposed AFC uses HTTP protocol to be applied through firewalls. It is implemented using basic trigger facilities of a commercial DBMS for practical purpose.

• Journal of Intelligence and Information Systems
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• v.12 no.3
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• pp.95-110
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• 2006

To enhance the rule-based reasoning capability of Semantic Web, the XRML (eXtensible Rule Markup Language) approach embraces the meta-information necessary for the extraction of explicit rules from Web pages and its maintenance. To effectuate the automatic identification of rules from unstructured texts, this research develops a framework of using rule ontology. The ontology can be acquired from a similar site first, and then can be used for multiple sites in the same domain. The procedure of ontology-based rule identification is regarded as a graph search problem with incomplete nodes, and an A* algorithm is devised to solve the problem. The procedure is demonstrated with the domain of shipping rates and return policy comparison portal, which needs rule based reasoning capability to answer the customer's inquiries. An example ontology is created from Amazon.com, and is applied to the many online retailers in the same domain. The experimental result shows a high performance of this approach.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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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)

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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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.