• Journal of Digital Convergence
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• v.14 no.7
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• pp.403-409
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• 2016

This study is a food shopping experience of omni-channel. Food threats and healthy living concerns bring different channels in led to increase reasonable way such as various demand. Omni-channels should be premised on understanding customer behavior as well as empirical user types in which considerations including the value of experience and understanding consumer behavior. Online survey result showed that, (1)offline food shopping, major retail store with quality, buy fresh food directly 2~3 times a month (2)online food shopping, e-commerce site with costs, buy fruits & nuts 2~3 times a month. After in-depth interview with eight high quality participants, I analyzed needs for food shopping experience in regard to the four steps food purchasing journey then derived a persona with integral value 'health' and 'diet'. It is classified into two types. One is the primary persona, family and health oriented, considering household money 'saving', and other is secondary persona, work and personal oriented, looking forward to 'automatic supply'. The result of this study provided an insight that help us explore ways to resolve function and services in the context of a healthy and balanced diet for improving food shopping experience of omni-channel.

• Journal of Digital Contents Society
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• v.4 no.2
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• pp.167-179
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• 2003

It has been widely researched in many studies what is the most effective way to learn English as a second language. It has been generally accepted that the most effective teaching method is to make students interested in learning of English. Teaching method has to be modified with the change of the times according to the demand of the students. The purpose of this study is to reveal that language teaming method through internet could be more effective than that of the existing teaching one by offline loaming. For this purpose some subject students were divided by two groups of the experimental and of the controlled. From the result of the study it could be derived that teaching method, by means of cyber lecture, make a great effect not only on the attitude but on the achievement of the students when they are in the class of English as a second language. This paper also shows what could be the typical model for the teaching method by internet based on the experiment. This kind of way of teaching is supported by the questionnaire which has much more positive response from the students who were in the member of experimental group. Finally such a experiment would be described based on the Web-oriented teaching method with the respect of education of digital contents.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.1
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• pp.29-42
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• 2019

In order to expand the qualitative aspects of public facility, this study used SNS data to derive user-oriented preference factors for public facilities and then were quantified in terms of supply side and demand side. To derive preference factor, LDA, one of topic modeling, was used and attraction index was calculated for each facility. In addition we analyzed spatial accessibility to measure the degree of service experience of users by using 2SFCA model. The study area covered public libraries of Seoul, Korea. As a result of study, five topics were extracted as preference factors for the public library: Circumstance, Scale of facility, Cultural program, Parenting, Books and materials. In particular topic of circumstance and parenting were newly derived preference factors unknown in previous studies. As a result of calculating attraction index for each library, the index of Songpa Library, Jungdok Library, and Namsan Library was high. Songpa library has received good evaluation in parenting factor, and Jungdok & Namsan library in circumstance factor. The accessibility of each region seems to better in center of Seoul where public libraries are crowded, but shrinking toward the outskirts. We expect that the proposed method will contribute to user-oriented public facility evaluation and policy decision making.

• Journal of Internet Computing and Services
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• v.10 no.3
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• pp.113-129
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• 2009

Abstract Corporate activities are composed of numerous working processes and during the working flow, various business processes are being created and completed simultaneously. Enterprise Resources Planning (ERP) makes the working process simple, yet creates more complicated work structure and therefore, there is an absolute need of efficient management for business processes. The workflow literature has been looking for efficient and effective ways of rediscovering and mining workflow intelligence and knowledge from their enactment histories and event logs. As part of studies to analyze and improve the process, the concepts of 'Process Mining', 'Process re-discovery', 'BPR (Business Process Reengineering)' have appeared and the studies for practical implementation are proactively being done. However, these studies normally follow the approach throughout data warehousing for log data of process instances. It is very hard for these approaches to reflect user's intention to the rediscovering and mining activities. The process instances designed based on the consideration of analysis can make groupings effectively and when the analysis demand of user changes within the analysis domain can also reduce the cost of analysis. Therefore, the thesis proposes a special type of workflow model, which is called a colored workflow model, that is extended from the ICN (information control net) modeling methodology by reinforcing the concept of colored token. The colored tokens represent the conceptual types of constraints and criteria that can be used to classifying and grouping the workflow intelligence and knowledge extracted from the corresponding workflow models' enactment histories and event logs. Through the runtime information of process instances, it makes possible to analyze proactive and user-oriented process with the goal of deriving business knowledge from the beginning of process definition.

• Journal of Korean Society of Transportation
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• v.31 no.3
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• pp.33-44
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• 2013

Personal Rapid Transit(PRT) has emerged as a promising transportation mode for transit-oriented sustainable communities. In this study, an alternative design of questionnaire survey is proposed in order to capture traveler's perception of an unexperienced transportation mode. This study aims at predicting the mode choice diversion behavior of potential PRT users who do not have experience of using it previously, considering their willingness-to-pay. The proposed model was applied to predict an aggregate forecast of PRT patronage for the city of Songdo where PRT is considered to be constructed. For validation of the proposed model, the price elasticity of PRT demand was analyzed, compared with existing models. The analysis results suggest that the proposed design of questionnaire survey is able to capture respondents' attitude and perception to unexperienced transportation mode in an effective manner. Also, they show that the proposed diversion rate model is more realistic than existing models in explaining the effects of users' willingness-to-pay for predicting PRT patronage.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.241-251
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• 2022

Diagnosis and management of customer's skin condition is an important essential function in the cosmetics and beauty industry. As the social media environment spreads and generalizes to all fields of society, the interaction of questions and answers to various and delicate concerns and requirements regarding the diagnosis and management of skin conditions is being actively dealt with in the social media community. However, since social media information is very diverse and atypical big data, an intelligent skin condition diagnosis system that combines appropriate skin condition information analysis and artificial intelligence technology is necessary. In this paper, we developed the skin condition diagnosis system SCDIS to intelligently diagnose and manage the skin condition of customers by processing the text analysis information of social media into learning data. In SCDIS, an artificial neural network model, AnnTFIDF, that automatically diagnoses skin condition types using artificial neural network technology, a deep learning machine learning method, was built up and used. The performance of the artificial neural network model AnnTFIDF was analyzed using test sample data, and the accuracy of the skin condition type diagnosis prediction value showed a high performance of about 95%. Through the experimental and performance analysis results of this paper, SCDIS can be evaluated as an intelligent tool that can be used efficiently in the skin condition analysis and diagnosis management process in the cosmetic and beauty industry. And this study can be used as a basic research to solve the new technology trend, customized cosmetics manufacturing and consumer-oriented beauty industry technology demand.

• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.145-157
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• 2004

The cycle length design model of the Korean traffic responsive signal control systems is devised to vary a cycle length as a response to changes in traffic demand in real time by utilizing parameters specified by a system operator and such field information as degrees of saturation of through phases. Since no explicit guideline is provided to a system operator, the system tends to include ambiguity in terms of the system optimization. In addition, the cycle lengths produced by the existing model have yet been verified if they are comparable to the ones minimizing delay. This paper presents the studies conducted (1) to find shortcomings embedded in the existing model by comparing the cycle lengths produced by the model against the ones minimizing delay and (2) to propose a new direction to design a cycle length minimizing delay and excluding such operator oriented parameters. It was found from the study that the cycle lengths from the existing model fail to minimize delay and promote intersection operational conditions to be unsatisfied when traffic volume is low, due to the feature of the changed target operational volume-to-capacity ratio embedded in the model. The 64 different neural network based cycle length design models were developed based on simulation data surrogating field data. The CORSIM optimal cycle lengths minimizing delay were found through the COST software developed for the study. COST searches for the CORSIM optimal cycle length minimizing delay with a heuristic searching method, a hybrid genetic algorithm. Among 64 models, the best one producing cycle lengths close enough to the optimal was selected through statistical tests. It was found from the verification test that the best model designs a cycle length as similar pattern to the ones minimizing delay. The cycle lengths from the proposed model are comparable to the ones from TRANSYT-7F.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.31-39
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• 2023

The purpose of this study is to propose a scientific performance evaluation framework for measuring and managing the overall outcome of complex types of projects that are linked to public demand-based commercialization, such as information system projects and public procurement, in integrated national R&D projects. In the case of integrated national R&D projects that involve multiple research institutes to form a single final product, and in the case of demand-based demonstration and commercialization of the project results, the existing evaluation system that evaluates performance based on the short-term outputs of the detailed tasks comprising the R&D project has limitations in evaluating the mid- and long-term effects and practicality of the integrated research products. (Moreover, as the paradigm of national R&D projects is changing to a mission-oriented one that emphasizes efficiency, there is a need to change the performance evaluation of national R&D projects to focus on the effectiveness and practicality of the results.) In this study, we propose a performance evaluation framework from a structural perspective to evaluate the completeness of each national R&D project from a practical perspective, such as its effectiveness, beyond simple short-term output, by utilizing the Hourglass model. In particular, it presents an integrated performance evaluation framework that links the top-down and bottom-up approaches leading to Tool-System-Service-Effect according to the structure of R&D projects. By applying the proposed detailed evaluation indicators and performance evaluation frame to actual national R&D projects, the validity of the indicators and the effectiveness of the proposed performance evaluation frame were verified, and these results are expected to provide academic, policy, and industrial implications for the performance evaluation system of national R&D projects that emphasize efficiency in the future.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.97-107
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• 2015

Up until recently, Korea has ranked the first place in UN e-Government Survey for three consecutive years. In keeping with such accomplishment, the size of budget execution has been consistently growing in accordance with Korea's Government 3.0 policy and vision, leading to increase in big-sized informatization projects in the business. Especially in mission critical public sector's infrastructure where it affects many people, growing demand for establishing high-quality information system with new technologies being brought to attention in order to meet the complex needs of citizens. National defense information system, being one of representative domains examples in the concerned area, established high military competency by applying breakthrough technology. Network-oriented national defense knowledge informatization was set as the vision in order to implement core roles in making efficient national defense management; and effort has been made to materialize the vision by making advancement in national defense's information system and its informatization implementation system. This research studies new quality index relevant to test and evaluation (T&E)of informatization business in national defense which is the representative example of mission critical public sector's infrastructure. We studied international standards and guidelines, analyzed actual T&E cases, and applied them to the inspection items that are currently in use, complying with the e-government law (Act No. 12346, Official Announcement Date 2014. 1.28., Enforcement Date 2014. 7.29.) As a result of productivity analysis, based on hypothesis in which suggested model was applied to T&E of the national defense informatization business, we confirmed the possibility of enhancement in the T&E productivity by assessing reliability, expertise, and safety as evaluation factors.