• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.777-787
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• 2020

With the development of the Internet and the spread of mobile devices, the online market is growing rapidly. As the number of customers using online shopping malls explodes, research is being conducted on the analysis of usage behavior from customer data, personalized product recommendations, and service development. Thus, this paper seeks to analyze the overall process of online shopping malls through process mining, and to identify the factors that influence users' purchases. The data used are from a large online shopping mall, and R was the analysis tool. The results show that customer activity was most prominent in categories with event elements, such as unconventional discounts and monthly giveaway events. On the other hand, searches, logins, and campaign activity were found to be less relevant than their importance. Those are very important, because they can provide clues to a customer's information and needs. Therefore, it is necessary to refine the recommendations from related search words, and to manage activity, such as coupons provided when customers log in. In addition to the previous discussion, this paper proposes various business strategies to enhance the competitiveness of online shopping malls and to increase profits.

• The Journal of Society for e-Business Studies
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• v.21 no.1
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• pp.147-163
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• 2016

Gender information of a smart device user is essential to provide personalized services, and multi-modal data obtained from the device is useful for predicting the gender of the user. However, the method for utilizing each of the multi-modal data for gender prediction differs according to the characteristics of the data. Therefore, in this study, an ensemble method for predicting the gender of a smart device user by using three classifiers that have text, application, and acceleration data as inputs, respectively, is proposed. To alleviate privacy issues that occur when text data generated in a smart device are sent outside, a classification method which scans smart device text data only on the device and classifies the gender of the user by matching text data with predefined sets of word. An application based classifier assigns gender labels to executed applications and predicts gender of the user by comparing the label ratio. Acceleration data is used with Support Vector Machine to classify user gender. The proposed method was evaluated by using the actual smart device log data collected from an Android application. The experimental results showed that the proposed method outperformed the compared methods.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.79-98
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• 2009

Web traffic is becoming an important indicator to make inferences about internet companies' future prospects so that traditional firm valuation methods need to be modified to integrate the ideas of web traffic information as a major asset of internet companies. It is because web traffic is a measure of attracting visitors to firm's web site and is the basis for internet companies' marketing expenditure and customer acquisition and retention. Also the web traffic represents the internet companies' technological advances and marketability. The major purpose of this study is to show the relevance of web traffic for valuation of internet companies. For this, we test hypothesis with the firm's web traffic and financial data using the analysis model of Hand(2000a) derived from the log-linear model introduced by Ye and Finn(1999). Test results show that the web traffic, more specifically the number of unique visitors, visits, and page views are all positively related to the firm's value. This implies that the web traffic information should be considered as one of the important non-financial indicator for the internet firm valuation.

• Journal of the Korean Society for information Management
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• v.24 no.2
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• pp.29-44
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• 2007

The purpose of this study was to explore whether the concept of amount of invested mental effort (AIME) developed in the field of educational psychology can help explain why people put so little effort into online searching. In this experimental study, two information retrieval systems - a web search engine and a university library system - were used to make a comparison. The data were collected from 15 undergraduate students through background questionnaires, think-aloud protocols, search logs, post-search questionnaires, and post-task interviews. The findings indicate that perception of the web's "easy-ness" and high levels of self confidence in searching capability led the subjects to put less effort into web searching than they do into library system searching. In addition, the perceived difficulty of search task influenced the extent of mental effort invested. The AIME proved a useful framework for understanding search behavior and user experience for both web search engines and library systems.

• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.19-26
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• 2010

Due to the development of new digital technologies in recent years, consumers play different roles with compared to the past. Though they used to consume various existing contents, nowadays they have opportunities to use in user-friendly environment which can make them create and share their own contents. Under this circumstance, the digital storytelling has become a big issue in large area, and the web-based contents have been produced with diverse contests. In fact, the digital storytelling means a kind of storytelling method which is made by digital technologies and has been created under various circumstances. However, many researches concern that the digital storytelling is just applied for moving from analog to digital in terms of a tool, but in the reality it is far from the web 2.0 with collective intelligence. Therefore, in this paper, we propose a web-based collaborative digital storytelling platform that analysis user's action processes and components applying collective intelligence.

• The KIPS Transactions:PartB
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• v.12B no.2 s.98
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• pp.181-190
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• 2005

Developing wireless internet service and mobile devices, mechanisms for web service across are various. However, the existing web infrastructure and content were designed for desktop computers and arc not well-suited for other types of accesses, e.g. PDA or mobile Phone that have less processing power and memory, small screens, limited input facilities, or network bandwidth etc. Thus, there is a growing need for transcoding techniques that provide that ability to browse the web through mobile devices. However, previous researches on existing web contents transcoding are service provider centric, which does not accurately reflect the user's continuously changing interest. In this paper, we presents a transcoding technique involved in making existing news contents based on XML available via customized wireless service, mobile phone.

• Journal of Information Technology Services
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• v.13 no.4
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• pp.231-243
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• 2014

Of the various data that corporations can approach, web log data are important data that correspond to data analysis to implement customer relations management strategies. As the volume of approachable data has increased exponentially due to the Internet and popularization of smart phone, web log data have also increased a lot. As a result, it has become difficult to expand storage to process large amounts of web logs data flexibly and extremely hard to implement a system capable of categorizing, analyzing, and processing web log data accumulated over a long period of time. This study thus set out to apply Hadoop, a distributed processing system that had recently come into the spotlight for its capacity of processing large volumes of data, and propose an efficient analysis plan for large amounts of web log. The study checked the forms of web log by the effective web log collection methods and the web log levels by using Hadoop and proposed analysis techniques and Hadoop organization designs accordingly. The present study resolved the difficulty with processing large amounts of web log data and proposed the activity patterns of users through web log analysis, thus demonstrating its advantages as a new means of marketing.

• Convergence Security Journal
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• v.2 no.2
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• pp.189-198
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• 2002

In this paper, we have designed the Web_based Home Gateway Management System using SSL, which can manage subscribers using Home Gateway device in home networking technology. To manage Home Gateway devices with SNMP, management elements are classified into system. performance. fault functional area based on MIB objects from Home Gateway devices MIB. System analysis provides configuration information of each Home Gateway, and Performance analysis provides device's traffic information and state. And fault analysis provides fault logging for the unexpected events and trap message from devices. HGMS uses SSL (Secure Socket Layer) to enforce the security of communication which is between server and client, and it improved the stability of HGMS.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.771-773
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• 2005

There have been researches about analyzing the information retrieval patterns of log file to efficiently obtain the users' information research patters in web environment. The methods frequently used in their researches is to suggest the algorithms by which the frequent one is derived from the path traversal patterns in efficient way. But one of their general problems is not to provide the proper solution in case of complex, that is, general topological patterns. Therefore this paper tries to suggest a efficient algorithm after defining the general information retrieval pattern.

• Annual Conference of KIPS
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• 2006.11a
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• pp.23-26
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• 2006

최근 대부분의 이러닝(E-Learning)을 교육하는 사이트는 학습 콘텐츠를 검색하는 방법이 단순한 리스트의 나열과 택스트 매칭(Text matching)방법을 사용하는 단점이 있다. 이를 보완하기 위해 좀 더 컴퓨터가 정보 데이터의 의미를 분석하여 검색이 가능하도록 개념 네트워크인 시맨틱웹(Semantic Web)이 등장하였다. 본 논문에서는 이러한 시맨틱웹의 온톨로지(Ontology) 언어 중에 토픽맵(Topic Maps)을 사용하여 많은 양의 학습 정보 데이터를 쉽고도 정확하게 연결 지어 학습 콘텐츠에 대한 정보를 표현하고, 구조화할 수 있는 방법을 모색해 보고자 한다. 학습자의 관심분야 정보, 학습객체의 학습 권장자의 정보와 함께 학습 경험과 검색 빈도수를 분석한 협력 필터링과 학습 에이전트의 개인화 기법을 동시에 사용하여 선호도를 분석한다. 이 선호도를 가지고 학습자의 메타데이터를 생성하고, 로그 데이터를 따로 데이터베이스에 저장한다. 이러한 학습자의 정보와 학습 콘텐츠간의 정보를 상호 연결하여, 그 토픽맵을 사용하여 연관관계를 정의해 줌으로써 학업성취도를 높이고, 학습자 개개인의 성향에 가장 알맞은 학습 콘텐츠를 탐색해가는 네비게이터(Navigator)를 설계하였다.