• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.302-304
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• 2011

Rule based machine learning techniques are very important in our real world now. We can list out some important application which we can apply rule based machine learning algorithm such as medical data mining, business transaction mining. The different between rules based machine learning and model based machine learning is that model based machine learning out put some models, which often are very difficult to understand by expert or human. But rule based techniques output are the rule sets which is in IF THEN format. For example IF blood pressure=90 and kidney problem=yes then take this drug. By this way, medical doctor can easy modify and update some usable rule. This is the scenario in medical decision support system. Currently, Rough set is one of the most famous theory which can be used for produce the rule. LEM2 is the algorithm use this theory and can produce the small set of rule on the database. In this paper, we present an improvement of LEM2 algorithm which incorporates the variable precision techniques.

• The Journal of Information Technology
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• v.8 no.1
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• pp.95-112
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• 2005

This paper will look into the Main Substance and Some Problems of 24 hours Advance Cargo Manifest Declaration Rule, focuses on the Policy implication of Korea's and counter measures of our parties concerned about 24-hour advance vessel manifest rule. The 24-hour rule requires cargo owners to submit cargo manifest information to U.S. Customs 24 hours before vessel sailing from the final foreign port to a U.S. port. Cargo manifest information must be complete, accurate, and timely. As of February 2, 2003 manifest information can be submitted by paper or electronically. Cargo descriptions must be at a level of detail consistent with Harmonized Tariff Schedule (HTS) codes used by U.S. Customs. HTS codes are 10 digits. Electronic submissions are made via the Automated Manifest System (AMS) run by U.S. Customs.

• Journal of Information Technology Services
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• v.2 no.2
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• pp.63-73
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• 2003

Insurance system has a lot of decision factors which are affected by the kinds of insurance products. and has the features of many exceptions. Since in applying the product attributes to the current system the value definition through tables and the exception treatment logic (if then else) are used in parallel, the cost of a product change and a new product development becomes increased and the prompt market reaction is difficult. In this paper, we propose the well formed rule base system which makes data for the business logic of insurance attributes and discuss the benefit of application of this system to the real project.

• Journal of Intelligence and Information Systems
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• v.11 no.3
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• pp.103-115
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• 2005

For today's enterprises to survive in the current rapidly changing business environments, it is imperative to make quick and successful decisions to various challenges. In making important business decisions, if enterprises utilize business rules and knowledge, properly and promptly, they may effectively reduce the chance of failures. However, in most of today's information systems, these rules and knowledge are not managed in centralized and systemic manner. They disperse over entire enterprises' information systems, and sometimes reside in the heads or memos of enterprises' employees. BRE (Business Rule Engines) is a solution that systematically and centrally manages these business knowledge and rules of an enterprise. With BREs, any business user is able to store, edit, retrieve and utilize business rules and knowledge in centralized repository, without IT development skills. And with BRE, enterprises could improve business intelligence and attain strategic advantages over other enterprises. However, since there is no clear criteria for BRE introductions, it is not easy to decide whether or not to introduce the expensive BRE solution to an enterprise. In this research we propose a decision model for BRE introduction. Using this model, business analysts considering BRE introduction, readily make decisions on BRE introduction.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.72-91
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• 2005

• Journal of the Korea Society of Computer and Information
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• v.18 no.1
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• pp.83-91
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• 2013

As humans perform many tasks using computers, HCI(Human-Computer Interaction) has received much attention these days. One objective of HCI research is to propose how to design more consistent systems. In order to evaluate the consistency of HCI devices from the user's perspective, several models such as TAG(Task Action Grammar), GOMS(Goals-Operators-Methods-Selection Rule), and GTN(General Transition Network) have been developed. TAG specifies actions to perform tasks in terms of rule schema. It has been verified that the less the number of rule schema is, the better users perform the tasks due to assumably higher consistency. This paper hypothesizes that the consistency of systems depends not only on the number of rule schema but also on the distances between rule schema. That is, the closer the rule schema are, the easier it is to acquire the whole set of rule schema. An experiment supported this hypothesis. Therefore, distances between rule schema should be considered as well as the number of rule schema when designing systems.

• The Journal of Fisheries Business Administration
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• v.46 no.1
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• pp.41-62
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• 2015

Though the debate of the establishment time, place and characteristic of the first fisheries cooperatives in Korea has been existed, it is clear that they had been established in the neighborhood of Tongyeong during the period of Japanese colonial rule. But the objective, motive and historical background of the first fisheries cooperatives are still slightly vague. To explain and explore that of fisheries cooperatives, over 1,000 articles of media, about 100 document of the Japanese government-general of Korea and the annals of the Joseon dynasty, many of research paper and others were included and refined. Through the result of that process, three core factors were founded that affect both the emergence of fisheries cooperatives and relationship of the them. The first one is about the royal family's fishing area where in the neighborhood of Tongyeong. The next one is Kentaro Kashii who was called king or devil of the Korean fisheries industry and the last one is fishermen and their organizations including fisheries cooperatives. Some of the above and the fishing rights conflict that make a guidance for understanding them has not been clearly mentioned. The result of this study shows that the birth of the first fisheries cooperatives were caused by not only the Japanese government-general of Korea but mainly Korean fishermen's voluntary activity and free will against fishing rights conflict between them. And both of the first fisheries cooperatives had somewhat difference in their constitution of organization and their characteristics but it was a tragic irony that the more exploitation and despoliation were conducted by Japanese government-general and Japanese capitalist the more movement and activity of Korean were frequently and severely occurred. Even though the findings and results of this study would be a proof and helpful material for understanding the establishment and their activity of fisheries cooperatives in Tongyeong, more research and contribution would be need by members of fisheries academic and business world.

• Journal of the Korea Convergence Society
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• v.10 no.2
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• pp.217-225
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• 2019

The Latent Growth Modeling(LGM) is known as the typical analysis method of longitudinal data and it could be classified into unconditional model and conditional model. It is common to assume that the growth trajectory of unconditional model of LGM is linear. In the case of quasi-linear, the methodology for improving the model fitness using Sequential Pattern of Association Rule Mining is suggested. To do this, we divide longitudinal data into quintiles and extract periodic changes of the longitudinal data in each quintiles and make sequential pattern based on this periodic changes. To evaluate the effectiveness, the LGM module in SPSS AMOS was used and the dataset of the Youth Panel from 2001 to 2006 of Korea Employment Information Service. Our methodology was able to increase the fitness of the model compared to the simple linear growth trajectory.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.4
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• pp.145-154
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• 2020

The paper aims to investigate the interactions between rule of law, economic growth and the shadow economy in 18 selected transition economies. This study uses annual data over the period 2002-2015 for 18 transition countries to estimate the effects of rule of law and other factors on the size of shadow economy. The transition country group is classified based on International Monetary Fund resources and is selected on the basis of the availability of data. The data examined in this research are derived from the World Bank, Worldwide Governance Indicators project and Working Paper from International Monetary Fund. This study employs GMM method. The results show that the economic growth indicators have negative and statistically significant impact on the shadow economy. Additionally, these results also reveal that in transition countries the size of shadow economy is negatively related to the quality of rule of law. However, the findings of this research also point out that there are positive relationships between inflation, public expenditure and the size of shadow economy. Hence the results from this study suggest that the size of shadow economy could be controlled by improving the effectiveness of rule of law and the growth of economy particularly in transition countries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.407-420
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• 2015

Natural Language Question Answering (NLQA) and Prescriptive Analytics (PA) have been identified as innovative, emerging technologies in 2015 by the Gartner group. These technologies require knowledge bases that consist of data that has been extracted from unstructured texts. Every business requires a knowledge base for business analytics as it can enhance companies' competitiveness in their industry. Most intelligent or analytic services depend a lot upon on knowledge bases. However, building a qualified knowledge base is very time consuming and requires a considerable amount of effort, especially if it is to be manually created. Another problem that occurs when creating a knowledge base is that it will be outdated by the time it is completed and will require constant updating even when it is ready in use. For these reason, it is more advisable to create a computerized knowledge base. This research focuses on building a computerized knowledge base for business using a supervised learning and rule-based method. The method proposed in this paper is based on information extraction, but it has been specialized and modified to extract information related only to a business. The business knowledge base created by our system can also be used for advanced functions such as presenting the hierarchy of technologies and products, and the relations between technologies and products. Using our method, these relations can be expanded and customized according to business requirements.