• Journal of KIISE:Databases
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• v.34 no.6
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• pp.528-545
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• 2007

Recently ontology has been an attractive technology along with the business strategy of providing a plenty of more intelligent services. The essential problem in application domains using ontology is that all members, agents, and application programs in the domains must share the same ontology concepts. However, a variety of mobile devices, sensing devices, and network components manufactured by various companies, a variety of common carriers, and a variety of contents providers make multiple heterogeneous ontologies more likely to coexist. We can see many past researches fallen into resolving this semantic interoperability. Such methods can be broadly classified into by-mapping, by-merging, and by-translation. In this research, we focus on by-translation among them which uses a translation rule directly made between two heterogeneous ontology data like OntoMorph. However, the manual composition of the direct translation rule is not convenient by itself and if there are N ontologies, the direct method has the rule composition complexity of $O(N^2)$ in the worst case. Therefore, in this paper we introduce the cascade composition of translation rules based on web openness in order to improve the complexity. The research result made us recognize some important factors in an ontology translation system, that is speediness of translation, and conveniency of translation rule composition, and some experiments and comparing analysis with existing methods showed that our cascade method has more conveniency with insuring the speediness and the correctness.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.127-141
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• 2017

Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.

• Journal of Internet Computing and Services
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• v.13 no.1
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• pp.65-74
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• 2012

Cloud services are used for improving business. Moreover, customer relationship management(CRM) approaches use social networking as tools to enhance services to customers. However, most cloud systems do not support the semantic structures, and because of this, vital information from social network sites is still hard to process and use for business strategy. This paper proposes a collaboration framework based on social semantic web for cloud system. The proposed framework consists of components to support social semantic web to provide an efficient collaboration system for cloud consumers and service providers. The knowledge acquisition module extracts rules from data gathered by social agents and these rules are used for collaboration and business strategy. This paper showed the implementations of processing of social network site data in the proposed semantic model and pattern extraction which was used for the virtual grouping of cloud service providers for efficient collaboration.

• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.39-51
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• 2004

Product factory is a real-time financial product design system for the Internet customers. The hybrid product is a product taking combined characteristics of two different products. Hybrid product factory is a product factory that designs hybrid products from two different products based on both business rules and customer requirements. Though the importance of product factory is emphasized in the industry, there has not been much research peformed regarding product factory. In this research, we developed a product factory system that designs hybrid products. To design a hybrid product, it is necessary to have a method to combine attributes and values of two different products, and a method to control the combining operations to properly reflect business requirements. In this research, we developed low different combining operators and business rule representations. rn addition, to prove the effectiveness of this methods, we implemented a prototypical system and demonstrated on cases regarding financial loan products.

• International Journal of Advanced Culture Technology
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• v.8 no.2
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• pp.96-100
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• 2020

In this paper, I try to help the business operation so that they could continue the desirable operation despite the unfavorable conditions. It is important to reinforce for corporate growth various support measures to generate profits. However, if they are involved in criminal activities such as slush fund creation, they will have to deal with them separately. As a result, To raise awareness helps to keep the company running. Recently, the companies are in a poor condition due to overseas migration. If a company does not create profits by doing business, it is a burden to continue operating and it will eventually be hard to support and destroy. The corporate crime and white-collar crime are mostly similar types, mainly because they occur in the industry. The corporate crime proceeds throughout the company and ultimately translates into corporate profits. The white-collar crime, on the other hand, is a profitable part of the individual. In the process of generating profits, the purpose and management method of slush funds is an important issue in judging whether illegal immorality of business is intended or not. In addition, in the case of the corporate crime, it seems necessary to identify the types of slush fund raising activities in addition to the investigations of the accomplices and clinical investigations, and to apply efficient investigation methods on a case-by-case basis. At present, many companies frequently migrate overseas due to the influence of domestic regulations. In this process, if it is involved in crime such as a borrowing accounts or the purpose of slush fund creation should be treated separately.

• The Journal of Society for e-Business Studies
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• v.19 no.1
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• pp.119-129
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• 2014

The renowned Pareto rule of 80/20 has been challenged in the electronic marketplace with the emergence of long tail economy. Mass customization on top of the Internet infrastructure is expected to explain these changes of product concentration. In this paper, we empirically analyzed the micro-transactional data of a Groupon-like daily deal web site to identify the changes of product and customer concentration. The results show the long tail pattern aligned with the previous research on the e-commerce literature on the long tail. We find that the notification setting on email or SMS about daily deal influences the patterns of sales concentration. The information through email and SMS is expected to enable consumers to know about daily bargains and purchase the coupons eventually. However, the email notification for niche products results in the decreased sales while the SMS notification for overall product promotes overall products.

• The KIPS Transactions:PartD
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• v.13D no.5 s.108
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• pp.691-698
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• 2006

The companies need the enterprise-wide support environment to build the competency to gather VOM(Voice of Market) in the process of preparing and implementing the strategy and to help establishing and managing the business process in order to secure the continuous competitive edge The enterprise-wide support environment to establish, operate, improve and evaluate the business process must be carried out. In this paper we define the method to define process and business rule in order to enable accurate execution of the process. Furthermore, collection and refection of accurate data concerning the competency of individuals, the subjects of the process execution, allows prevention of weakness of the process execution result and is the basis for identifying the areas of improvement. Therefore, high visibility can be attained through the work knowledge and processes presented in rules, and it can help firmly establish the process centered work culture (or system) in the organization by process improvement strictly based on data.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.1-13
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• 2022

With the development of network technologies, the security to protect organizational resources from internal and external intrusions and threats becomes more important. Therefore in recent years, the anomaly detection algorithm that detects and prevents security threats with respect to various security log events has been actively studied. Security anomaly detection algorithms that have been developed based on rule-based or statistical learning in the past are gradually evolving into modeling based on machine learning and deep learning. In this study, we propose a deep-autoencoder model that transforms LSTM-autoencoder as an optimal algorithm to detect insider threats in advance using various machine learning analysis methodologies. This study has academic significance in that it improved the possibility of adaptive security through the development of an anomaly detection algorithm based on unsupervised learning, and reduced the false positive rate compared to the existing algorithm through supervised true positive labeling.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.287-308
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• 2022

The explosive growth of cryptocurrency, led by Bitcoin has emerged as a major issue in the financial market recently. As a result, interest in cryptocurrency investment is increasing, but the market opens 24 hours and 365 days a year, price volatility, and exponentially increasing number of cryptocurrencies are provided as risks to cryptocurrency investors. For that reasons, It is raising the need for research to reduct investors' risks by dividing cryptocurrency which is not suitable for recommendation. Unlike the previous studies of maximizing returns by simply predicting the future of cryptocurrency prices or constructing cryptocurrency portfolios by focusing on returns, this paper reflects the tendencies of investors and presents an appropriate recommendation method with interpretation that can reduct investors' risks by selecting suitable Altcoins which are recommended using Apriori algorithm, one of the machine learning techniques, but based on the similarity and association rules of Bitocoin.

• IE interfaces
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• v.25 no.1
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• pp.79-86
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• 2012

Traditional business process management systems provide verification tools of process models to deploy and automate the models. However, there are not so many studies on how to maintain systematically collaborative process models such as supply chain processes when companies are willing to change and update the collaborative process models. In this paper, we analyze change patterns of collaborative processes and declare 19 change patterns. In addition, we apply the change patterns to the process interoperability patterns in order to identify the change problems in case of independent process changes of collaborative processes. As a result, we devise an independency checking algorithm of process changes in collaborative processes.