• The Journal of Society for e-Business Studies
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• v.26 no.4
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• pp.133-154
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• 2021

Today smart phone is the most common media with a vehicle called 'application'. In order to understand how media users select applications and build their repertoire, this study conducted two-step approach using big data from smart phone log for 4 weeks in November 2019, and finally classified 8 media repertoire groups. Each of the eight media repertoire groups showed differences in time spent of mobile application category compared to other groups, and also showed differences between groups in demographic distribution. In addition to the academic contribution of identifying the mobile application repertoire with large scale behavioral data, this study also has significance in proposing a two-step approach that overcomes 'outlier issue' in behavioral data by extracting prototype vectors using SOM (Sefl-Organized Map) and applying it to k-means clustering for optimization of the classification. The study is also meaningful in that it categorizes customers using e-commerce services, identifies customer structure based on behavioral data, and provides practical guides to e-commerce communities that execute appropriate services or marketing decisions for each customer group.

• Korean Journal of Construction Engineering and Management
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• v.16 no.5
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• pp.65-76
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• 2015

FEED (Front End Engineering and Design) is the key area that determines the competitiveness of procurement and construction in the EPC contracts especially in terms of the added value. Nevertheless, previous researches in FEED have been limited to the process and deliverable of design work or the particular management business function (e.g. System Engineering, collaboration, information etc.). In this context, the purpose of this study is to propose a comprehensive FEED structure and its functions from the project management perspective throughout the whole project life-cycle for thermal power plants. Proposed FEED business procedures are classified into three levels; First level is the classification of FEED business phases, the second level defines major FEED management functions, and the third level is detailed FEED functions. A survey using proposed FEED functions and assessment variable was conducted in order to analyze the current status and the areas for future improvement. It is expected that the proposed structure, functions, and evaluation methodology for FEED management will contribute to effective practice of FEED as well as to improvement of competitive capability for engineering, procurement, and construction (EPC) companies.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.4
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• pp.97-105
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• 2016

As information system is getting higher and amount of information assets is increasing, skills of threatening subjects are more advanced, so that it threatens precious information assets of ours. The purpose of this study is to present a strategic direction for the types of companies seeking access to information security. The framework classifies companies into eight types so company can receive help in making decisions for the development of information security strategy depending on the type of company it belongs to. Paired comparison method survey conducted by a group of information security experts to determine the priority and the relative importance of information security management elements. The factors used in the security response strategy are the combination of the information security international certification standard ISO 27001, domestic information protection management system certification K-ISMS, and personal information security management system certification PIMS. Paired comparison method was then used to determine strategy alternative priorities for each type. Paired comparisons were conducted to select the most applicable factors among the 12 strategic factors. Paired comparison method questionnaire was conducted through e-mail and direct questionnaire survey of 18 experts who were engaged in security related tasks such as security control, architect, security consulting. This study is based on the idea that it is important not to use a consistent approach for effective implementation of information security but to change security strategy alternatives according to the type of company. The results of this study are expected to help the decision makers to produce results that will serve as the basis for companies seeking access to information security first or companies seeking to establish new information security strategies.

• Journal of the Korean Society for information Management
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• v.21 no.4 s.54
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• pp.5-26
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• 2004

A literature analysis for the planning and realization of the multimedia meta database and digital library's integrated information system was carried out to establish the various oceanographic resources in the Oceanographic Information Center, the first in Korea. The study targeted from printed matter, network resources, full-text and to VOD. The focus of the analysis lies in the providing practical integrated information retrieval service for oceanographic resources based on the framework of effective MODS metadata with network resources description. The analyses included oceanographic resources, multimedia information processing, MODS metadata descriptive elements, metadata classification, system organization, and retrieval for planning and implementation of the multimedia meta database system.

• The Journal of Society for e-Business Studies
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• v.20 no.1
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• pp.217-241
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• 2015

One of the challenges in online community management is what level of perceived anonymity can be granted to encourage active participation from members while discouraging unhealthy activities. Few studies developed a scheme of anonymity and investigated how different levels of anonymity influence community activities. This study develops a classification scheme of anonymity encompassing the following three different levels : (1) real name(no anonymity), (2) nickname (partial anonymity), and (3) random assignment of a temporal ID (complete anonymity). Then, it examines how different levels of anonymity influence trust and perceived risk, which in turn affect virtual team performance. A series of laboratory experiments were performed, manipulating the levels of anonymity, in the context of well-structured communities that allow prior interactions among community members. The data was collected from 364 laboratory participants and analyzed using ANOVA and PLS. The results indicate that the difference of anonymity between (2) and (3) had not be significant and the only (1) could not guarantee the anonymity. The impact of anonymity on trust and perceived risk could not be significant in this situation. These findings could contribute to make more beneficial member identification strategies in online community practice.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.83-96
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• 2018

Extracting keywords representing documents is very important because it can be used for automated services such as document search, classification, recommendation system as well as quickly transmitting document information. However, when extracting keywords based on the frequency of words appearing in a web site documents and graph algorithms based on the co-occurrence of words, the problem of containing various words that are not related to the topic potentially in the web page structure, There is a difficulty in extracting the semantic keyword due to the limit of the performance of the Korean tokenizer. In this paper, we propose a method to select candidate keywords based on semantic similarity, and solve the problem that semantic keyword can not be extracted and the accuracy of Korean tokenizer analysis is poor. Finally, we use the technique of extracting final semantic keywords through filtering process to remove inconsistent keywords. Experimental results through real web pages of small business show that the performance of the proposed method is improved by 34.52% over the statistical similarity based keyword selection technique. Therefore, it is confirmed that the performance of extracting keywords from documents is improved by considering semantic similarity between words and removing inconsistent keywords.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.133-148
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• 2014

Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.227-231
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• 2009

The aim of the research is to focus on the cognitive principles and to achieve human-level intelligence in referring context based browser history and the Windows history. One of the major problems faced by today's computer users is insufficient and single exclusive context based reference of the browser history and the Windows history. Today we search for the browser history and Windows history in different places even though the context is the same. For e.g., When working on a research paper or preparing a business presentation, a user may require to refer many web sites on the internet and various documents on the local computer. The browser can provide only time based history. The windows document history is also time based and limited to list only few documents. Hence, we propose a tool "Cognitive Based Context Aware Reference History Management Tool" which helps to access the exclusive reference of context and time based history in one place. The tool also proposes to store image history with urls and classifies images of a specific topic accessed in different time, bookmarks management and cross browser history management. These features are very useful as we can access all related documents (doc, docx, ppt, pptx, pdf, txt, and html), web pages, images and bookmarks in one place. The tool uses the cognitive principles like classification and association to achieve the purpose.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.243-248
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• 2011

According to increasing of accessing multimedia stream contents, Web services have become popular. However, these Web services are not supported with the same quality to Web clients who frequently access multimedia services. This paper proposes ontological technique to apply client level Quality of Service(QoS) that provides two different levels to serve Web service with proper quality by contribution value. And, it describes with UML(Unified Modeling Language) how to relate QoS and SLA(Service Level Agreement). Main contribution of this paper is to support client level QoS and SLA and to use Ontology for it. Therefore, this work uses an ontology-based approach to organize QoS and SLA, enabling semantic classification of all Web services based on domains and QoS and SLA attributes.