• Proceedings of the Korea Association of Information Systems Conference
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• 2001.12a
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• pp.413-426
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• 2001

본 연구는 첫째, 정보통신기술의 발달이 시각장애인 복지 증진에 미칠 수 있는 가능성에 대한 이론적인 부분을 고찰하였다. 둘째, 우리나라 시각장애인 정보화의 문제점과 해결책을 도출하였고 셋째, 시각장애 사용자를 위한 전자상거래 인터페이스 디자인의 분석 및 설계를 통해 전자상거래에서 시각장애 사용자들이 큰 제약없이 사용할 수 있는 방안을 제시하고자 한다. 현재 시각장애인들의 웹 사용을 보면 시각장애 전용 S/W의 보조 하에 사용을 하고 있다. 그러한 보조 도구의 실정에 맞도록 텍스트 버전 및 Non-Frame버전, Alt-Text 옵션, 캡션 등을 넣어 접근성을 확보하고 인터넷을 큰 제약을 받지않고 이용할 수 있도록 웹 페이지의 설계가 필요한 실정이다. 이를 위하여 먼저 시각장애에 대한 개념과 원인 및 종류 그리고 특성을 통해 시각장애인에 대한 이론적 배경을 파악하였다. 그리고 시각장애인의 정보화 환경과 이용 현황과 시각장애인의 정보 접근을 제도적, 기기 및 소프트웨어 개발 측면에서 분석을 하였고, 장애인을 위한 정보통신기술 중 대표적인 사례를 검토해 보았다. 다음으로 국내외의 대표적인 전자상거래 사이트에서의 인터페이스를 화면구성(Layout), 텍스트(Text), 그래픽(Graphic), 멀티미디어(MultiMedia) 측면에서 분석을 하였다. 분석한 내용을 바탕으로 시각장애 사용자의 입력(User Input) 부분을 고려한 인터페이스 방향을 제시하고 프로토타입을 개발하여 시험 대상 사이트와의 만족도를 시각장애 사용자를 통해 비교 ·분석하였다. 결론부분에서는 정보불평등을 해소하고, 정보통신기술이 장애인의 복지향상에 기여하도록 하기 위해 전자상거래 싸이트에서의 시각 장애인들을 위한 방향을 제시하고자 한다.박의 표현, 등록 및 색인방법 (c) 공급 선박의 분류와 표현 방법 (d) 에이전트의 정보 수집을 위한 메시지 표현 방법 (e) 수집된 선박정보의 데이터베이스 저장 표현방법 (f) 요구 선박을 찾아주는 정보제공 서비스가 요구된다.동을 보여 조사대상 5호분, 6호분, 7호분, 중 가장 심한 거동을 보이고 있다. 이는 고분 벽돌의 깨짐이 6호분이 가장 심하다는 사실과 무관하지 않은 것으로 판단된다. 봉분내부의 토양층구조에 대한 지오레이다 영상단면을 분석한 결과 무령왕릉 연도상부의 누수지방지층이 심하게 균열되어 있음을 발견하였다. 이 곳은 고분내부로 직접누수가 발생하는 곳이다. 직접누수와 지하수 형태로 유입된 침투수는 고분군 주위의 지반의 함수비를 증가시켜 지반의 지지력을 약화시키고 또한 고분내로 서서히 유입되어 고분내부의 습도를 100%로 유지시키는 주된 원인이다. 이러한 높은 습도는 고분내의 남조류의 번식을 가져왔으며 남조류의 번식은 현재 6호분이 가장 심각하고 7호분이 우려되는 수준이며 5호분은 문제가 없는 것으로 판단된다. 이와 같이 고분군의 발굴후 인위적인 환경변화와 지속적인 강우침투 및 배수 불량의 영향은 고분군의 안정성에 상당한 위험을 초래하였으며, 현 상태는 각 고분에 대한 보강이 불가피한 것으로 판단된다. 고분 벽돌의 깨짐, 고분 벽체의 거동, 조류의 서식등을 포함하여 송산리 고분군에서 발생되고 있는 보존상의 제반 문제점들을 일차적으로 누수 및 침투수에 의한 결과이다. 그러므로 무엇보다도 고분군 내부 및 고분 주변으로의 강우 및 지하수 침투를 막는 차수 대책이 시급한 것으로 판단된다. 또한 이미 발생한 변위가 더 이상 진행되지 않도록 하중을 경감하고 토압의 균형을 이루는 보강대책이 시급한 실정이다. 고분군

• Information Systems Review
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• v.16 no.3
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• pp.1-23
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• 2014

This research uses the Analytical Hiararchy Process (AHP) to scientifically and systematically calculate the weightings of attributes as well as dimensions considered for assessing an informational website. The present paper aims at observing and using the computed weightings to comparatively examine the perceptions of customer users and business users. We use the 3C-D-T (i.e., Content, Community, Commerce, Design, and Technology) framework to conduct a case study where we review and assess restaurant websites and calculate attribute weightings on these websites. Data used for website review was collected in two phases. Data in the first phase was collected from customer users, and data in the second phase was from business users who had registered in the same websites. Users were instructed to perform a pairwise comparison of the relative importance of website attributes. Our data analysis revealed that the customer users and business users demonstrated different views on the relative importance of the individual attributes. Based on the findings, we suggested that business users of restaurants should adapt their views to the customers' views to minimize perceptional differences, thereby increasing customer satisfaction and accomplishing successful business outcomes.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.2
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• pp.19-29
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• 2011

Due to the development of web technologies and the increasing use of smart devices such as smart phone, in recent various web services are widely used in many application fields. In this environment, the topic of supporting personalized and intelligent web services have been actively researched, and an analysis technique on a web-click stream generated from web usage logs is one of the essential techniques related to the topic. In this paper, for efficient analyzing a web-click stream of sequences, a sequential pattern mining technique is proposed, which satisfies the basic requirements for data stream processing and finds a refined mining result. For this purpose, a concept of interesting sequential patterns with a time-interval constraint is defined, which uses not on1y the order of items in a sequential pattern but also their generation times. In addition, A mining method to find the interesting sequential patterns efficiently over a data stream such as a web-click stream is proposed. The proposed method can be effectively used to various computing application fields such as E-commerce, bio-informatics, and USN environments, which generate data as a form of data streams.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.599-604
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• 2005

This paper presents fuzzy rules to predict diagnosis of Wisconsin breast cancer with minimized number of feature in put using the neural network with weighted fuzzy membership functions (NEWFM) and the non-overlap area distribution measurement method. NEWFM is capable of self-adapting weighted membership functions from the given the Wisconsin breast cancer clinical training data. n set of small, medium, and large weighted triangular membership functions in a hyperbox are used for representing n set of featured input. The membership functions are randomly distributed and weighted initially, and then their positions and weights are adjusted during learning. After learning, prediction rules are extracted directly from n set of enhanced bounded sums of n set of small, medium, and large weighted fuzzy membership functions. Then, the non-overlap area distribution measurement method is applied to select important features by deleting less important features. Two sets of prediction rules extracted from NEWFM using the selected 4 input features out of 9 features outperform to the current published results in number of set of rules, number of input features, and accuracy with 99.71%.

• The Journal of the Korea Contents Association
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• v.19 no.5
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• pp.187-193
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• 2019

Key-value storage engines are an essential component of growing demand in many computing environments, including social networks, online e-commerce, and cloud services. Recent key-value storage engines offer many features such as transaction, versioning, and replication. In a key-value storage engine, transaction processing provides atomicity through Write-Ahead-Logging (WAL), and a synchronous commit method for transaction processing flushes log data before the transaction completes. According to our observation, flushing log data to persistent storage is a performance bottleneck for key-value storage engines due to the significant overhead of fsync() calls despite the various optimizations of existing systems. In this article, we propose a group synchronization method to improve the performance of the key-value storage engine. We also design and implement a transaction scheduling method to perform other transactions while the system processes fsync() calls. The proposed method is an efficient way to reduce the number of frequent fsync() calls in the synchronous commit while supporting the same level of transaction provided by the existing system. We implement our scheme on the WiredTiger storage engine and our experimental results show that the proposed system improves the performance of key-value workloads over existing systems.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.319-325
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• 2021

With the advancement of wireless technology and the rapid growth of the infrastructure of mobile communication technology, systems applying AI-based platforms are drawing attention from users. In particular, the system that understands users' tastes and interests and recommends preferred items is applied to advanced e-commerce customized services and smart homes. However, there is a problem that these recommendation systems are difficult to reflect in real time the preferences of various users for tastes and interests. In this research, we propose a Fuzzy-AHP-based movies recommendation system using the Gated Recurrent Unit (GRU) language model to address a problem. In this system, we apply Fuzzy-AHP to reflect users' tastes or interests in real time. We also apply GRU language model-based models to analyze the public interest and the content of the film to recommend movies similar to the user's preferred factors. To validate the performance of this recommendation system, we measured the suitability of the learning model using scraping data used in the learning module, and measured the rate of learning performance by comparing the Long Short-Term Memory (LSTM) language model with the learning time per epoch. The results show that the average cross-validation index of the learning model in this work is suitable at 94.8% and that the learning performance rate outperforms the LSTM language model.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.41-63
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• 2023

As the online e-commerce market growing, the need for a recommender system that can provide suitable products or services to customer is emerging. Recently, many studies using the sentiment score of online review have been proposed to improve the limitations of study on recommender systems that utilize only quantitative information. However, this methodology has limitation in extracting specific preference information related to customer within online reviews, making it difficult to improve recommendation performance. To address the limitation of previous studies, this study proposes a novel recommendation methodology that applies deep learning technique and uses various linguistic factors within online reviews to elaborately learn customer preferences. First, the interaction was learned nonlinearly using deep learning technique for the purpose to extract complex interactions between customer and product. And to effectively utilize online review, cognitive contents, affective contents, and linguistic style matching that have an important influence on customer's purchasing decisions among linguistic factors were used. To verify the proposed methodology, an experiment was conducted using online review data in Amazon.com, and the experimental results confirmed the superiority of the proposed model. This study contributed to the theoretical and methodological aspects of recommender system study by proposing a methodology that effectively utilizes characteristics of customer's preferences in online reviews.

• 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.22 no.3
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• pp.129-142
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• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.1-31
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• 2017

Modern businesses are adopting new technologies to serve their markets better as well as to improve efficiency and productivity. The advertising industry has continuously experienced disruptions from the traditional channels (radio, television and print media) to new complex ones including internet, social media and mobile-based advertising. This case study focuses on proposing intelligent advertising business model in Seoul's metro network. Seoul has one of the world's busiest metro network and transports a huge number of travelers on a daily basis. The high number of travelers coupled with a well-planned metro network creates a platform where marketers can initiate engagement and interact with both customers and potential customers. In the current advertising model, advertising is on illuminated and framed posters in the stations and in-car, non-illuminated posters, and digital screens that show scheduled arrivals and departures of metros. Some stations have digital screens that show adverts but they do not have location capability. Most of the current advertising media have one key limitation: space. For posters whether illuminated or not, one space can host only one advert at a time. Empirical literatures show that there is room for improving this advertising model and eliminate the space limitation by replacing the poster adverts with digital advertising platform. This new model will not only be digital, but will also provide intelligent advertising platform that is driven by data. The digital platform will incorporate location sensing, e-commerce, and mobile platform to create new value to all stakeholders. Travel cards used in the metro will be registered and the card scanners will have a capability to capture traveler's data when travelers tap their cards. This data once analyzed will make it possible to identify different customer groups. Advertisers and marketers will then be able to target specific customer groups, customize adverts based on the targeted consumer group, and offer a wide variety of advertising formats. Format includes video, cinemagraphs, moving pictures, and animation. Different advert formats create different emotions in the customer's mind and the goal should be to use format or combination of formats that arouse the expected emotion and lead to an engagement. Combination of different formats will be more effective and this can only work in a digital platform. Adverts will be location based, ensuring that adverts will show more frequently when the metro is near the premises of an advertiser. The advertising platform will automatically detect the next station and screens inside the metro will prioritize adverts in the station where the metro will be stopping. In the mobile platform, customers who opt to receive notifications will receive them when they approach the business premises of advertiser. The mobile platform will have indoor navigation for the underground shopping malls that will allow customers to search for facilities within the mall, products they may want to buy as well as deals going on in the underground mall. To create an end-to-end solution, the mobile solution will have a capability to allow customers purchase products through their phones, get coupons for deals, and review products and shops where they have bought a product. The indoor navigation will host intelligent mobile-based advertisement and a recommendation system. The indoor navigation will have adverts such that when a customer is searching for information, the recommendation system shows adverts that are near the place traveler is searching or in the direction that the traveler is moving. These adverts will be linked to the e-commerce platform such that if a customer clicks on an advert, it leads them to the product description page. The whole system will have multi-language as well as text-to-speech capability such that both locals and tourists have no language barrier. The implications of implementing this model are varied including support for small and medium businesses operating in the underground malls, improved customer experience, new job opportunities, additional revenue to business model operator, and flexibility in advertising. The new value created will benefit all the stakeholders.