• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.123-136
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• 2014

Recently, online shopping has further developed as the use of the Internet and a variety of smart mobile devices becomes more prevalent. The increase in the scale of such shopping has led to the creation of many Internet shopping malls. Consequently, there is a tendency for increasingly fierce competition among online retailers, and as a result, many Internet shopping malls are making significant attempts to attract online users to their sites. One such attempt is keyword marketing, whereby a retail site pays a fee to expose its link to potential customers when they insert a specific keyword on an Internet portal site. The price related to each keyword is generally estimated by the keyword's frequency of appearance. However, it is widely accepted that the price of keywords cannot be based solely on their frequency because many keywords may appear frequently but have little relationship to shopping. This implies that it is unreasonable for an online shopping mall to spend a great deal on some keywords simply because people frequently use them. Therefore, from the perspective of shopping malls, a specialized process is required to extract meaningful keywords. Further, the demand for automating this extraction process is increasing because of the drive to improve online sales performance. In this study, we propose a methodology that can automatically extract only shopping-related keywords from the entire set of search keywords used on portal sites. We define a shopping-related keyword as a keyword that is used directly before shopping behaviors. In other words, only search keywords that direct the search results page to shopping-related pages are extracted from among the entire set of search keywords. A comparison is then made between the extracted keywords' rankings and the rankings of the entire set of search keywords. Two types of data are used in our study's experiment: web browsing history from July 1, 2012 to June 30, 2013, and site information. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The original sample dataset contains 150 million transaction logs. First, portal sites are selected, and search keywords in those sites are extracted. Search keywords can be easily extracted by simple parsing. The extracted keywords are ranked according to their frequency. The experiment uses approximately 3.9 million search results from Korea's largest search portal site. As a result, a total of 344,822 search keywords were extracted. Next, by using web browsing history and site information, the shopping-related keywords were taken from the entire set of search keywords. As a result, we obtained 4,709 shopping-related keywords. For performance evaluation, we compared the hit ratios of all the search keywords with the shopping-related keywords. To achieve this, we extracted 80,298 search keywords from several Internet shopping malls and then chose the top 1,000 keywords as a set of true shopping keywords. We measured precision, recall, and F-scores of the entire amount of keywords and the shopping-related keywords. The F-Score was formulated by calculating the harmonic mean of precision and recall. The precision, recall, and F-score of shopping-related keywords derived by the proposed methodology were revealed to be higher than those of the entire number of keywords. This study proposes a scheme that is able to obtain shopping-related keywords in a relatively simple manner. We could easily extract shopping-related keywords simply by examining transactions whose next visit is a shopping mall. The resultant shopping-related keyword set is expected to be a useful asset for many shopping malls that participate in keyword marketing. Moreover, the proposed methodology can be easily applied to the construction of special area-related keywords as well as shopping-related ones.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.37-47
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• 2019

The purpose of this paper is to construct a system that matches the patient's image disease information with the medical image viewer in providing the medical image information to the medical staff. Currently, medical image information systems that are commercialized mostly provide only one image viewer with various image information of diseases or use incompatible exclusive viewers. For this reason, we designed and implemented a medical image information viewer matching system that integrates and provides specialized viewers that can be selected by diseases' image information. That is, it is a system to match and view medical image viewers based on disease information extracted from tag information stored as the metadata in DICOM file, which is medical image information standard, for disease-specific viewer matching. We analyzed the execution performances through our retrieval service of medical image information from our implementation system, and showed compatibility and control with various viewers.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.167-168
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• 2016

2015년 대한민국을 관통하는 키워드 중 하나가 O2O (Online to Offline)이다. 대기업과 중소기업까지 O2O를 전면에 내세우고 새로운 비즈니스를 모색하는 기업들이 확대되고 있다. O2O 비즈니스 기업들의 업종도 모바일 메신저, 포털을 비롯한 인터넷 사업자, 통신사업자, 모바일 기기 제조사, 전자상거래 기업과 같은 정보통신기술(ICT) 관련 기업부터 유통기업까지 다양하다. 본 연구를 위하여 2014.5~2015.5 1년 간 여자대학생 92명의 '모바일 쇼핑 금융서비스 이용 현황' 조사를 실시하였다. 조사 결과, 상품정보검색(96.7%), 구매(88%), 주문 배송(77.2%), 할인 프로모션정보(62%) 등 모바일 쇼핑을 하였고, 모바일금융 서비스로는 하루에 1번정도 (20.4%), 월1~3회(20.4%), 1주 3~4회(16.3%), 이용 안하는 경우(24.5%)로 조사 되었다. 모바일 금융서비스를 이용하지 못하는 이유로는 개인정보보안 및 해킹우려(39%), 이용/결제 과정의 불편함(25.8%) 등 이었으며, 국내 O2O 플랫폼이 제공하는 서비스는 쇼핑부터 결제까지 완결적인 구조로 보완할 부분이 필수적이다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.4
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• pp.193-201
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• 2017

Financial firms strengthen to protect personal information from the leakage, introducing various security solutions such as print output security, internet network Isolation system, isolationg strorage of customer information, encrypting personal information, personal information detecting system, data loss prevention, personal information monitoring system, and so on. Financial companies are also entering the era of cutthroat competition due to accept of the new channels and the paradigm shift of financial instruments. Accordingly, The needs for security for customer information held by financial firms are keep growing. The large security accidents from the three card companies on January 2014 were happened, the case in which one of the outsourcing personnel seized customer personal information from the system of the thress card companies and sold them illegally to a loan publisher and lender. Three years after the large security accidents had been passed, nevertheless the security threat of the IT outsourcing workforce still exists. The governments including the regulatory agency realted to the financail firms are conducting a review efforts to prevent the leakage of personal information as well as strengthening the extent of the sanction. Through the analysis on the application of security policy for outsourcing personnel in case of large-scale Financial IT projects and the case study of appropriate security policies for security compliance, the theis is proposing a solution for both successfully completing large-scale financial IT Project and so far as possible minizing the risk from the security accidents by the outsouring personnel.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Journal of Internet Computing and Services
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• v.13 no.4
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• pp.103-119
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• 2012

In this paper, we conducted for information search process of users, who have experience to buy paid-application in Appstore, to find out difference of information searching efforts and information sources, reliance on the information in term of attributes of Apps and buyes' innovativenss. As a result, Innovative buyers take a effort to search various information source but they get help buying decision-making supports by own their efforts like searching in category. On the other hand, non-Innovative buyers who search informtion less than Innovative buyers, get information like recommendation from friend and Apps, then they keep those information help buying decision-making supports highly. Besides buyers of Utility Apps search on objective information sources mainly, but those sources have influence on buying decision-making supports. On the other side buyers of Enjoyment Apps consider reliance on the information more than information searching efforts to buying decision-making supports as they get information source like popularity. This research suggests operators of Appstore and app developers how to promote their Apps to users.

• The Journal of Society for e-Business Studies
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• v.23 no.3
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• pp.145-157
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• 2018

The social curation service that selectively provides information generated by individuals or groups with the same interests can have a synergistic effect when combined with the recently used SNS-based chatting function. If these kinds of chatting-based curation technologies are applied to the Internet shopping malls, particularly, buyers can obtain more reliable information in real time basis, and sellers can provide them with more differentiated and rich information in a continuous manner. This research suggests a chatting-based commerce platform that provides the social curation service based on chats among sellers, existing buyers, and potential buyers. The proposed commerce platform can organize a chat channel for each store and product not only to immediately respond to new and existing customer inquiries about stores, brands, and detailed products, but also to continuously activate differentiated sales strategies to customers subscribed to the channel. In particular, MongoDB is used to permanently save and archive the information and chatting history of each channel, so that the buyer can search and refer to them recorded in the corresponding channel at any time.

• The Journal of the Korea Contents Association
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• v.15 no.10
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• pp.535-546
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• 2015

Using mobile bookstore application is increasing in accordance with the proliferation of internet and mobile devices. In this circumstances, the purpose of this study is to explore the factors regarding to mobile bookstore application quality evaluation are effecting to customer satisfaction and reuse intention. Thus information, transaction, design, communication, ubiquitous access, personalization, safety are set as a quality evaluation factor. And it is analyzed the effect of these factors to customer satisfaction and reuse intention with path analysis method. As the result, transaction, ubiquitous access, personalization, safety factors effected customer satisfaction and customer satisfaction effected reuse intention. These results show that user mainly access mobile bookstore application for purchasing book. Thus it is important for mobile bookstore operator to keep the application accessed anytime, and serve personalized information, and simplify transaction process, and intensify security for protecting personal or transaction information.

• Information Systems Review
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• v.7 no.1
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• pp.97-113
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• 2005

This paper designed and implemented(programmed) a comparative shopping agent that helps consumers to shop at on-line shopping malls over Internet. At offline stores, as consumers usually tell a sales clerk about a manufacturer, functions and price range of an item they want to purchase, the sales clerk will show the products or relevant catalogues. Then the consumer will compare functions, design and prices of the product and buy it with the lowest price. PriceMeter, a comparative shopping agent, introduced in this paper, is designed best geared to this consumers' buying behavior. Basically, as consumers enter a manufacturer's name, price, features and etc. at a search window, PriceMeter will search the web and provide a list of product informations such as features and prices that meet the search conditions. Consumers can see the information in either a form of catalogue or a printing format. As consumers click specific items to examine closely, it will show prices and information about shopping malls that sell the requested items. Clicking a 'Buy' icon, the consumers will be transferred to the right web page at the linked shopping mall. The emergence of the comparative shopping agent will expedite a consumer-centered retailing economy in the age of e-commerce. As consumers are provided with a better set of product and shopping mall information, they can make better purchasing decisions and gain more bargaining power shifted from manufacturers(sellers). The presentation of this comparative shopping agent is intended to promote the consumer-centered B2C e-commerce.

• The Journal of Korean Association of Computer Education
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• v.7 no.2
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• pp.77-84
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• 2004

In this thesis, We propose the self-directed learning and test-performing abilities assessment method to evaluate the learning and the test-performing abilities in which learners can not only control their own learning abilities for themselves, but also judge objectively learning and test-performing abilities. This method shows the membership degree of learning and test-performing abilities by using both the triangle-type membership function and the fuzzy logic. In addition, it gives the fuzzy grades to each item. The final membership degrees are calculated and the fuzzy grades are decided by the operation and composition of fuzzy relations on the membership degrees of learning and test-performing abilities. In this method, which is applicable to a writing subject for information searchers, learners are asked to analyse the membership degrees of the learning and test-performing abilities and the final fuzzy grades and to adjust a learning process for themselves.