• The Journal of Society for e-Business Studies
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• v.25 no.1
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• pp.109-121
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• 2020

The purpose of this study is to find out primary policies for reducing PM(particulate matter) as well as for improving the quality of life. Serious particulate matters cause to diverse healthcare and economy problems including business transactions. Unfortunately, until recently there are very few researches regarding the decision-making process for particulate matter policies. This study has applied the AHP(Analytic Hierarchy Process) method to develop cooperative policy making processes. The upper layer of this hierarchy analysis consists of four parts, i.e., transportation, production facility, living environment, and urban planning management. And each upper layer parts has their own three policies. 25 experts including policy-makers, academic researchers and industrial specialists have decided the primary policies and directions. The most significant PM policy is the mandatory reduction of air pollution and suspension of factory operation in the production industry. The results of this study can lead to guidelines for making environmental policies.

• 한국IT서비스학회:학술대회논문집
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• 2002.06a
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• pp.96-107
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• 2002

21세기를 맞이하는 기업은 세계화와 국제화, 정보의 다양화 및 분산화, 제품수명주기의 단축과 수익률의 감소, 고품질의 제품, 지식산업의 등장, 고객 욕구의 증대 등 다양한 내 ${\cdot}$ 외적 환경변화에 대응해야 하고 경쟁우위를 확보하기 위해 끊임없이 기업의 변화능력을 확보해야 한다. 기업은 제품을 생산하여 판매하는 기업의 시스템 전체가 경쟁력을 갖추어 총체적 우위를 확보하고 고객의 기호와 감성에 호소하는 제품을 만들 수 있도록 다양한 정보를 제공하는 시스템을 필요로 하게 되었다. 즉, 시간과 서비스에 뒤진 고객지원체계는 기업의 성장에 절대적인 마이너스 요인이므로 고객 정보의 효율적 관리, 고객 요구에 대한 신속한 대응, 정기적 고객만족도 조사 등 고객이 원하는 것을 재빠르게 얻어낼 수 있는 고객 친밀형 정보시스템의 확립이 요구되는 것이다. 이를 해결해 줄 수 있는 새로운 경영정보시스템이 바로 WebERP인 것이다. WebERP는 기업의 원활한 자재, 구매활동을 위해 제안된 MRP에서 시작되었으며, 생산관리의 개념을 포함하고 있는 MRPII로 확대되었다가 다시 인사나 회계, 재무 등 조직이나 기업의 전업무영역을 수용하는 종합경영정보시스템으로 발전된 것이다. 현재는 전자상거래와 관련하여 WebERP의 필요성이 부각되었고, 모기업과 협력사간의 구매발주 및 납품관리를 확대시켜 소모성자재(MRO: Maintenance, Repair & Operation) 및 기타 공동구매서비스와 유사업종간의 그룹을 형성하여 ASP(Application Service Provider)의 공동 환경을 이용한 WebERP 환경을 이용한 WebERP활용이 현시점으로 적실히 필요한 것이다. 이는 중소기업들의 공동 협업체제를 도모하여 외세를 대비하는 응집력을 확고히 함으로써 집단체제의 e마켓플레이스 확립과 더불어 국제경쟁을 대비한 방안으로서 집단 공동의 웹환경 인프라가 필요한 것이다. 이러한 배경에서 ASP환경을 이용한 WebERP활용방안과 시스템 구축전략을 경남지역의 기계산업정보화사업단이 추진하는 일반기계, 전기기계, 금속기계, 수송기계, 정밀기계의 5개 업종을 4,877개 기업 대상으로 추진하고 있으며, 그 중 WebERP의 모델로서 2003년도까지 50개 기업을 선정하고, 이는 5개 업종 골고루 선정하여 적용과 표준화 모델로 전개해 나가는 것을 정리한 것이다.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.1-18
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• 2022

As digital technology converges into the e-commerce market across industries, online transactions have activated, and the use of online has increased. With the recent spread of infectious diseases such as COVID-19, this market flow is accelerating, and various product information can be provided to customers online. Providing a variety of information provides customers with various opportunities but causes difficulties in decision-making. The recommendation system can help customers to make a decision more effectively. However, the previous research on recommendation systems is limited to only quantitative data and does not reflect detailed factors of products and customers. In this study, we propose an intelligent recommendation system that quantifies the attributes of products and customers by applying text mining techniques to qualitative data based on online reviews and integrates the existing objective indicators of total star rating, sentiment, and emotion. The proposed integrated recommendation model showed superior performance to the overall rating-oriented recommendation model. It expects the new business value to be created through the recommendation result reflecting detailed factors of products and customers.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.311-328
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• 2022

Because of the spread of smartphones due to the development of information and communication technology, online shopping mall services can be used on computers and mobile devices. As a result, the number of users using the online shopping mall service increases rapidly, and the types of products traded are also growing. Therefore, to maximize profits, companies need to provide information that may interest users. To this end, the recommendation system presents necessary information or products to the user based on the user's past behavioral data or behavioral purchase records. Representative overseas companies that currently provide recommendation services include Netflix, Amazon, and YouTube. These companies support users' purchase decisions by recommending products to users using ratings, purchase records, and clickstream data that users give to the items. In addition, users refer to the ratings left by other users about the product before buying a product. Most users tend to provide ratings only to products they are satisfied with, and the higher the rating, the higher the purchase intention. And recently, e-commerce sites have provided users with the ability to vote on whether product reviews are helpful. Through this, the user makes a purchase decision by referring to reviews and ratings of products judged to be beneficial. Therefore, in this study, the correlation between the product rating and the helpful information of the review is identified. The valuable data of the evaluation is reflected in the recommendation system to check the recommendation performance. In addition, we want to compare the results of skipping all the ratings in the traditional collaborative filtering technique with the recommended performance results that reflect only the 4 and 5 ratings. For this purpose, electronic product data collected from Amazon was used in this study, and the experimental results confirmed a correlation between ratings and review usefulness information. In addition, as a result of comparing the recommendation performance by reflecting all the ratings and only the 4 and 5 points in the recommendation system, the recommendation performance of remembering only the 4 and 5 points in the recommendation system was higher. In addition, as a result of reflecting review usefulness information in the recommendation system, it was confirmed that the more valuable the review, the higher the recommendation performance. Therefore, these experimental results are expected to improve the performance of personalized recommendation services in the future and provide implications for e-commerce sites.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.1-17
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• 2023

With the recent rapid development of ICT(Information and Communication Technology) and the popularization of digital devices, the size of the online market continues to grow. As a result, we live in a flood of information. Thus, customers are facing information overload problems that require a lot of time and money to select products. Therefore, a personalized recommender system has become an essential methodology to address such issues. Collaborative Filtering(CF) is the most widely used recommender system. Traditional recommender systems mainly utilize quantitative data such as rating values, resulting in poor recommendation accuracy. Quantitative data cannot fully reflect the user's preference. To solve such a problem, studies that reflect qualitative data, such as review contents, are being actively conducted these days. To quantify user review contents, text mining was used in this study. The general CF consists of the following three steps: user-item matrix generation, Top-N neighborhood group search, and Top-K recommendation list generation. In this study, we propose a recommendation algorithm that applies an extended similarity measure, which utilize quantified review contents in addition to user rating values. After calculating review similarity by applying TF-IDF, Word2Vec, and Doc2Vec techniques to review content, extended similarity is created by combining user rating similarity and quantified review contents. To verify this, we used user ratings and review data from the e-commerce site Amazon's "Health and Personal Care". The proposed recommendation model using extended similarity measure showed superior performance to the traditional recommendation model using only user rating value-based similarity measure. In addition, among the various text mining techniques, the similarity obtained using the TF-IDF technique showed the best performance when used in the neighbor group search and recommendation list generation step.

• Journal of the Korea Society of Computer and Information
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• v.8 no.4
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• pp.92-103
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• 2003

Great many advertisements are serviced in on-line by development of electronic commerce and internet user's rapid increase recently. However, this advertisement service is stopping in one-side service of relevant advertisement rather than doing users' inclination analysis to basis. Therefore, want advertisement service that many websites are personalized for efficient service of relevant advertisement and service through relevant server's log analysis research and enforce. Take advantage of log data of local system that this treatise is not analysis of server log data and analyze user's Preference degree and inclination. Also, try to propose advertisement system personalized by making relevant site tributary category and give weight of relevant tributary. User's preference user preference which analysis is one part of cooperation fielder ring of web personalized techniques use information in visit site tributary and suppose internet user's action in visit number of times of relevant site and try inclination analysis of mixing form. Express user's preference degree by vector, and inclination analysis result uninterrupted data that simplicity application form is not regarded and techniques that propose inclination analysis change of data since with move data use and analyze newly and proposed so that can do continuous renewal and application as feedback Sikkim. Presented method that can choose advertisements of relevant tributary through this result and provide personalized advertisement service by applying process such as user inclination analysis in advertisement chosen.

• Journal of Internet Computing and Services
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• v.6 no.2
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• pp.141-152
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• 2005

Many organizations have been recognizing the necessity of workflow automation technologies according to the rapid expansion of business process oriented applications, such as enterprise resource pianning, customer relationship management, electronic approval management, and so on, Thus, they have started adopting workflow management systems as an essential technological solution for their workflow processes, However, we need some technological extensions and improvements on them in order to accommodate a new type of workflow processes, which is called cross-organizational global workflow processes that require a certain level of collaborations between the organizations engaged in the global workflow processes, Fragment-driven workflow modeling methodology is a Bottom-Up methodology composing a global workflow by defining each organization's own activities, which is called a fragment through a realtime cooperative system. The approach is able to not only simplify the modeling work but also keep each organization's independence in modeling a global workflow, In this paper, we describe the fragment-driven workflow modeling methodology and realize the methodology through the implementation of a cooperative swimlane workflow modeling system.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.987-993
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• 2002

최근 들어 전자상거래의 급속한 발달로 전 세계적으로 수송 물동량이 급격히 증대되고 있고, 이로 인해 택배사업이 대단히 활성화되고 있다. 출발지와 목적지가 서로 상이한 무수히 만은 수송 요구가 들어오면 수송 요구화물의 신속한 집배송을 위한 배차계획 및 수송계획을 세우는 것이 택배회사의 주요 업무이다. 이러한 배차 계획 및 수송 계획을 어떻게 수립하느냐에 따라 전체 수송비용뿐만 아니라 고객들의 서비스 수준에 상당한 영향을 미치게 된다. 그러나 이러한 운영적 차원에서의 의사결정 이전에 훨씬 중요하게 고려해야 할 내용이 택배네트워크의 설계 문제이다. 이러한 택배네트워크의 설계에는 터미널 개수 및 위치를 결정하는 전략적 문제와 영업소들을 터미널에 할당하는 전술적 문제로 구분될 수 있다. 현재 우리 국내에는 크고 작은 수많은 택배사업자들이 있으나, 그 중에서 비교적 규모가 큰 주요 택배회사들은 대부분 전국에 걸쳐 다수의 터미널을 설치하여 두고 수송화물의 집배송을 위한 물류거점으로 운영하고 있다. 이와 같은 터미널 위치 및 개수가 정해진 상태에서 전국에 걸쳐 분포되어 있는 영업소들을 어떤 터미널에 할당하여 처리되도록 하느냐의 여부는 수송비용 측면에서뿐만 아니라 고객들에 대한 서비스 측면에서 대단히 중요한 의사결정 중의 하나이다. 본 연구에서는 비용과 시간을 고려하여 전국에 걸쳐 분포되어 있는 영업소들을 어떤 터미널에 할당해야 하는지를 결정하기 위한 수리적 모형을 제시하고, 이에 대한 탐색적 해법을 제시하며, 국내의 택배회사 사례를 대상으로 모형을 적용해 보고자 한다.무가 많이 발생하는 유통 분야의 프랜차이즈 산업을 대상으로 기업정보시스템 구현 및 경쟁력 강화를 뒷받침하기 위해서, 기업간 프로세스 협업(collaboration) 부분의 데이터 및 서식, 이를 취급하는 기능과 프로세스에 대란 분석을 통해 업무 프로세스 모델링 방법론과 관련한 모델링 지침 및 메타모델을 이용한 표준 업무 프로세스 모델을 개발하여 기업간 업무 프로세스 표준화에 대한 체계적인 관리에 대한 방안을 연구하고자 한다.의Bullwhip effect를 감소시킬 수 있는 장점이 있다. 동시에 이것은 향후 e-Business 시스템 구축을 위한 기본 인프라 역할을 수행할 수 있게 된다. 많았고 년도에 따른 변화는 보이지 않았다. 스키손상의 발생빈도는 초기에 비하여 점차 감소하는 경향을 보였으며, 손상의 특성도 부위별, 연령별로 다양한 변화를 나타내었다.해가능성을 가진 균이 상당수 검출되므로 원료의 수송, 김치의 제조 및 유통과정에서 병원균에 대한 오염방지에 유의하여야 할 것이다. 확인할 수 있었다. 이상의 결과에 의하면 고농도의 유기물이 함유된 음식물쓰레기는 Hybrid Anaerobic Reactor (HAR)를 이용하여 HRT 30일 정도에서 충분히 직접 혐기성처리가 가능하며, 이때 발생된 $CH_{4}$를 회수하여 이용하면 대체에너지원으로 활용 가치가 높은 것으로 판단된다./207), $99.2\%$(238/240), $98.5\%$(133/135) 및 $100\%$ (313)였다. 각

• Information Systems Review
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• v.23 no.3
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• pp.51-75
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• 2021

Global e-commerce websites offer personalized recommendation services to gain sustainable competitiveness. Existing studies have offered personalized recommendation services using quantitative preferences such as ratings. However, offering personalized recommendation services using only quantitative data has raised the problem of decreasing recommendation performance. For example, a user gave a five-star rating but wrote a review that the user was unsatisfied with hotel service and cleanliness. In such cases, has problems where quantitative and qualitative preferences are inconsistent. Recently, a growing number of studies have considered review data simultaneously to improve the limitations of existing personalized recommendation service studies. Therefore, in this study, we identify review and rating mismatches and build a new user profile to offer personalized recommendation services. To this end, we use deep learning algorithms such as CNN, LSTM, CNN + LSTM, which have been widely used in sentiment analysis studies. And extract sentiment features from reviews and compare with quantitative preferences. To evaluate the performance of the proposed methodology in this study, we collect user preference information using real-world hotel data from the world's largest travel platform TripAdvisor. Experiments show that the proposed methodology in this study outperforms the existing other methodologies, using only existing quantitative preferences.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.29-56
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• 2021

Recently, various types of products have been launched with the rapid growth of the e-commerce market. As a result, many users face information overload problems, which is time-consuming in the purchasing decision-making process. Therefore, the importance of a personalized recommendation service that can provide customized products and services to users is emerging. For example, global companies such as Netflix, Amazon, and Google have introduced personalized recommendation services to support users' purchasing decisions. Accordingly, the user's information search cost can reduce which can positively affect the company's sales increase. The existing personalized recommendation service research applied Collaborative Filtering (CF) technique predicts user preference mainly use quantified information. However, the recommendation performance may have decreased if only use quantitative information. To improve the problems of such existing studies, many studies using reviews to enhance recommendation performance. However, reviews contain factors that hinder purchasing decisions, such as advertising content, false comments, meaningless or irrelevant content. When providing recommendation service uses a review that includes these factors can lead to decrease recommendation performance. Therefore, we proposed a novel recommendation methodology through CNN-based review usefulness score prediction to improve these problems. The results show that the proposed methodology has better prediction performance than the recommendation method considering all existing preference ratings. In addition, the results suggest that can enhance the performance of traditional CF when the information on review usefulness reflects in the personalized recommendation service.