• Archives of design research
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• v.12 no.2
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• pp.75-86
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• 1999

Faced with various challenges given from internal or external, universities and colleges, about 300 in number, began to feel one another as their rival and recognized advertising marketing as a solution of unlimited competition and were in serious action for advertising. School advertising has suddenly increased in quantity, but falls in creative strategy of qualitative level behind corporate or product advertising. This study suggests effective creative strategy, based on comprehension of characteristics of school advertising and on analysis of creative as a strategy, of advertisements put by schools. School advertising demands the approach different from commercial dimension of corporate advertising, because schools are characteristic of public interests. Schools need to take positive creative strategy of image advertising instead of passive type of recruitment announcement. This effective and Positive creative strategy des not only Provide the chance to select schools for customers of school advertising which sets up applicants for admission as the first target audience, but also instills affirmative and positive image in potential customers of the future such as pupils of elementary or middle schools and ordinary people so that it may make effective communication possible

• Archives of design research
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• v.17 no.1
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• pp.373-382
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• 2004

Large sized buildings have proliferated in the modern world in order to solve problems caused by the growth of cities, and this trend has lead to diversity in style and utilization of interiors. In particular, wayfinding within the shopping mall for a customer is a common problem, so if customers do not have to face these navigational problems and could find the way more easily, then shopping mall or the store can expect much better profits. Therefore, directional sign system that can provide accurate and speedy information for customers with diversified ages, gender and knowledge is necessary. Firstly, the definition and element of all directional sign are analyzed through the existing literature survey. The elements are based on the practical elements and six categories such as type, pictogram, color, layout, form, and location were considered. Secondly, directional signs in large underground shopping mall was considered, and the speciality of the underground shopping mall was investigated, and finally top three underground shopping malls was analyzed. Thirdly, through the questionnaire, an objective appraisal of directional signs and problems was developed, and following possible improvement was suggested. This study has its own limitations since it is only applicable to the specific locations, however, directional designs will be useful in other types of buildings as well. Through the continuous studies of the users' psychology, these kinds of studies will be related to the environmental characteristics of various places.

• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.1-17
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• 2008

Data mining is defined as the process of discovering meaningful and useful pattern in large volumes of data. In particular, finding associations rules between items in a database of customer transactions has become an important thing. Some data structures and algorithms had been proposed for storing meaningful information compressed from an original database to find frequent itemsets since Apriori algorithm. Though existing method find all association rules, we must have a lot of process to analyze association rules because there are too many rules. In this paper, we propose a new data structure, called a Frequent Pattern Network (FPN), which represents items as vertices and 2-itemsets as edges of the network. In order to utilize FPN, We constitute FPN using item's frequency. And then we use a clustering method to group the vertices on the network into clusters so that the intracluster similarity is maximized and the intercluster similarity is minimized. We generate association rules based on clusters. Our experiments showed accuracy of clustering items on the network using confidence, correlation and edge weight similarity methods. And We generated association rules using clusters and compare traditional and our method. From the results, the confidence similarity had a strong influence than others on the frequent pattern network. And FPN had a flexibility to minimum support value.

• Journal of KIISE
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• v.42 no.5
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• pp.591-600
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• 2015

SLA (Service Level Agreement) is an essential factor that must be guaranteed to provide a reliable and consistent service to user in cloud computing environment. Especially, a contract between user and service provider with SLA is important in an environment using a cloud service brokerage. The cloud computing is classified into IaaS, PaaS, and SaaS according to IT resources of the various cloud service. The existing SLA is difficult to reflect the quality factors of service, because it only considers factors about the physical Network environment and have no methodological approach. In this paper, we suggested a method to specify the quality characteristics of software and proposed a mechanism and structure that can exchange SLA specification between the service provider and consumer. We defined a meta-model for the SLA specification in the SaaS level, and quality requirements of the SaaS were described by the proposed specification language. Through case studies, we verified proposed specification language that can present a variety of software quality factors. By using the UDDI-based mediation process and architecture to interchange this specification, it is stored in the repository of quality specifications and exchanged during service binding time.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.581-591
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• 2005

Collaborative filtering systems have problems involving sparsity and the provision of recommendations by making correlations between only two users' preferences. These systems recommend items based only on the preferences without taking in to account the contents of the items. As a result, the accuracy of recommendations depends on the data from user-rated items. When users rate items, it can be expected that not all users ran do so earnestly. This brings down the accuracy of recommendations. This paper proposes a collaborative recommendation method for extracting typical group preferences using user-item matrix optimization and user profiles in collaborative tittering systems. The method excludes unproven users by using entropy based on data from user-rated items and groups users into clusters after generating user profiles, and then extracts typical group preferences. The proposed method generates collaborative user profiles by using association word mining to reflect contents as well as preferences of items and groups users into clusters based on the profiles by using the vector space model and the K-means algorithm. To compensate for the shortcoming of providing recommendations using correlations between only two user preferences, the proposed method extracts typical preferences of groups using the entropy theory The typical preferences are extracted by combining user entropies with item preferences. The recommender system using typical group preferences solves the problem caused by recommendations based on preferences rated incorrectly by users and reduces time for retrieving the most similar users in groups.

• The KIPS Transactions:PartD
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• v.16D no.1
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• pp.55-64
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• 2009

Various business model and computing environments are currently merged into web services and many web related application products are also develop. Most of IT enterprises in Korea use the Service-oriented architecture (SOA) whenever they develop the web applications. SOA is an approach to loosely coupled, protocol independent, standards-based distributed computing where software resources available on the network are considered as Services. SOA is believed to become the future enterprise technology solution that promises the agility and flexibility the business users have been looking for by leveraging the integration process through composition of the services spanning multiple enterprises. But, There are no specific development methodology to apply into SOA standard model until now. The developer uses the currently existing methodology to develop the application with SOA. The users have some limitations to use it. In this paper, we suggest a Frameworks for applying agile methodology into SOA to address the productivity and quality of small web related project. We design and implement a frameworks architecture for applying the agile method into SOA and describe the process model to implement it. We finally evaluate the frameworks with productivity, flexibility and maintainability.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.5
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• pp.105-113
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• 2019

This study focuses on analyzing the key success factors of the traditional market by analyzing the impact on the business performance of merchants in the traditional market. Based on the existing research on entrepreneurship, psychological characteristics, capabilities, and physical characteristics of the market were considered as merchant characteristics. As a result, the risk characteristics and merchant pride, which are internal characteristics of merchants, have a significant effect on business performance. Among the competencies of merchants, product competency, customer management competency, and price competency were found to have a significant impact on business performance. Among the physical characteristics of the market, reputation and product diversity were found to have a significant effect on business performance. The results of this study are meaningful to empirically prove the relevance of the merchant's internal characteristics and capabilities, and the market's physical characteristics to the business performance of market merchants. Could be utilized. However, the limitation of this study is that there may be differences in each industry in the case of market merchant products. In future studies, empirical studies on the trader's relevance to business capability and business performance should be continued.

• Journal of vocational education research
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• v.37 no.1
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• pp.77-100
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• 2018

The purpose of this study was to explore how the college students of the beauty department changed their perspective about jobs, career development, and self-reflection in the process of job shadowing. We selected 8 students who were enrolled in the beauty department of a college located in the capital area and conducted individual in-depth interviews with them. According to the result of the study, First, participants have learned attitude, knowledge, competence, and an important matter necessary to achieve a excellent performance in the beauty job. In particular, they figured out that a core competency for a successful beauty job is to share a social relationship with the clients. Second, they recognized that doing a beauty job is very tough and then set a criteria to make a decision of their future job. Doing a Job shadowing made them being change in the area of job and their job perspective. Finally, they looked themselves back how they are satisfying with developing their career at the beauty department. This study is significantly meaningful in that it contextually showed how the college students are developing their job careers through an indirect job experience from a constructive point of view. Moreover, this study is different from the existing studies focusing on student's direct work experiences such as the existing internship programs, which focused on exploring the student's indirect work experience and the process of their vicarious learning. The result of the current study has a practical implication in terms of providing a basic perspective for career education for students of colleges.

• The Journal of Bigdata
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• v.3 no.2
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• pp.59-70
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• 2018

In order to provide stable district heat supplying service to the certain limited residential area, it is the most important to forecast the short-term future demand more accurately and produce and supply heat in efficient way. However, it is very difficult to develop a universal heat demand forecasting model that can be applied to general situations because the factors affecting the heat consumption are very diverse and the consumption patterns are changed according to individual consumers and regional characteristics. In particular, considering all of the various variables that can affect heat demand does not help improve performance in terms of accuracy and versatility. Therefore, this study aims to develop a demand forecasting model using deep learning based on only limited information that can be acquired in real time. A demand forecasting model was developed by learning the artificial neural network of the Tensorflow using past data consisting only of the outdoor temperature of the area and date as input variables. The performance of the proposed model was evaluated by comparing the accuracy of demand predicted with the previous regression model. The proposed heat demand forecasting model in this research showed that it is possible to enhance the accuracy using only limited variables which can be secured in real time. For the demand forecasting in a certain region, the proposed model can be customized by adding some features which can reflect the regional characteristics.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.87-94
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• 2019

In this paper, the method of analyzing the unformatted data of consumers accumulated on social networks in the era of the Fourth Industrial Revolution by utilizing data from the service design and social psychology aspects was proposed. First, the fandom phenomenon, which shows subjective and collective behavior in a space on a social network rather than physical space, was defined from a data service perspective. The fandom model has been transformed into a collective level of customer Persona that has been analyzed at a personal level in traditional service design, and social network analysis that analyzes consumers' big data has been presented as an efficient way to pattern and visually analyze it. Consumer data collected through social leasing were pre-processed by column based on correlation, stability, missing, and ID-ness. Based on the above data, the company's brand strategy was divided into active and passive interventions and the effect of this strategic attitude on the growth direction of the consumer's fandom community was analyzed. To this end, the fandom model of consumers was proposed by dividing it into four strategies that the brand strategy had: stand-alone, decentralized, integrated and centralized, and the fandom shape of consumers was proposed as a growth model analysis technique that analyzes changes over time.