• 한국IT서비스학회:학술대회논문집
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• 2008.05a
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• pp.546-551
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• 2008

기업들 간의 경쟁이 치열해짐에 따라 기업들은 최종 제품뿐만 아니라 최종 제품을 생산하는 프로세스에 대한 효율에 관심을 갖기 시작하였다. 또한 기업들은 자사의 핵심 역량을 중심으로 기업 구조를 재편하고, 핵심 역량 이외의 부분은 아웃소싱을 맡기고 있다. 이러한 상황에서 기업은 다양한 파트너들과 비즈니스 프로세스를 공유하고, 결합 관계에 따라 유연하게 구성할 수 있어야 한다. 기업의 비즈니스 프로세스에 대한 중요성을 일찍 깨닫고 많은 기업들이 프로세스 혁신 작업을 수행하였다. 몇몇 기업은 주목할만한 큰 성공을 거두었음에도 불구하고 기대했던 성과를 거두지 못한 체 많은 비용만 소모한 기업도 존재한다. 이와 같은 일들은 비즈니스 프로세스 디자인을 위한 방법에 대한 개선이 필요함을 시사한다. 하지만 대부분의 방법은 프로세스 디자인을 시각화하고, 평가하는데 중점을 둔 분석 도구의 기능에 편중되어 있으며, 여전히 프로세스의 실제 디자인을 지원하는 형식적인 모델링에 대한 연구는 여전히 부족한 실정이다. 따라서 본 논문에서는 비즈니스 프로세스의 모델링의 응용 분야로 기업 어플리케이션 통합으로 각광받고 있는 웹 서비스를 선택, 웹 서비스로 이루어진 프로세스의 최적화된 디자인을 지원하는 수리 모델을 제시하고자 한다.

• Annual Conference of KIPS
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• 2004.05a
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• pp.633-636
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• 2004

웹 서비스는 이질적인 응용 시스템들 사이의 연동 및 통합을 위한 표준화된 수단을 제공한다. 본 논문에서는 기존 웹 서비스들을 이용하여 정의되는 복합 웹 서비스를 효율적으로 실행하기 위한 방안으로서 사용자 에이전트를 이용한 분산 처리 시스템 구조 및 처리 방법을 제안한다. 본 시스템은 웹 서비스들의 통신 QoS 및 복합 웹 서비스의 부하 등을 고려하여 복합 웹 서비스의 호출 및 통합 작업을 사용자 에이전트에게 동적으로 위임하여 분산 처리함으로써 복합 웹 서비스의 성능 및 가용성을 향상시킨다.

• Annual Conference of KIPS
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• 2007.05a
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• pp.757-760
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• 2007

최근 각 기업과 공공기관들의 서비스들은 웹 서비스 기반으로 구축되고 있다. 이러한 여러 서비스들을 혼합한 협업적 웹 서비스 환경에서 서비스에 대한 프로세스 관리, 서비스 상태 체크, 서비스 과금을 위한 과금 요소 추출등을 위한 모니터링이 필요하다. 이에 본 논문에서는 효과적인 모니터링을 위하여 이벤트 기반의 협업적 웹 서비스 관리 모니터링 구조와 방법을 제시한다. SOA (Service Oriented Architecture) 기반의 협업적 웹 서비스 개발 생명주기를 단계별로 검토하여 관리 모니터링의 관점에서 필요한 요구사항들을 찾아내고, 이를 기반으로 협업적 웹 서비스 관리를 위한 이벤트 기반의 관리 모니터링 시스템 아키텍처를 제시한다.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.5
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• pp.359-368
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• 2013

Mashup is a web application that combines several different sources to create new services using Open APIs(Application Program Interfaces). Although the mashup has become very popular over the last few years, there are several challenging issues when combining a large number of APIs into the mashup, especially when composite APIs are manually integrated by mashup developers. This paper proposes a novel algorithm for automatic Open API composition. The proposed algorithm consists of constructing an operation connecting graph and searching composition candidates. We construct an operation connecting graph which is based on the semantic similarity between the inputs and the outputs of Open APIs. We generate directed acyclic graphs (DAGs) that can produce the output satisfying the desired goal. In order to produce the DAGs efficiently, we rapidly filter out APIs that are not useful for the composition. The algorithm is evaluated using a collection of REST and SOAP APIs extracted from ProgrammableWeb.com.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.807-814
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• 2021

As economic activities decrease, and the stock market decline due to COVID-19, many people are jumping into stock investment as an alternative source of income. As people's interest increases, many stock price analysis studies are underway to earn more profits. Due to the variance observed in the stock markets, it is necessary to analyze each stock independently and consistently. To solve this problem, we designed and implemented models and services that analyze stock prices using a reinforcement learning technique called Asynchronous Advantage Actor-Critic(A3C). Stock market data reflected external factors such as government bonds and KOSPI (Korea Composite Stock Price Index) as well as stock prices. Our proposed work provides a web service with a visual representation of predictions of stocks and stock information through which directions are given to investors to make safe investments without analyzing domestic and foreign stock market trends.

• Journal of the Semiconductor & Display Technology
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• v.9 no.3
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• pp.17-21
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• 2010

As the compact and easily accessible handheld devices, such as cellular phones and MP3 players equipped with image acquisition functionality, are becoming widely available among common users, various applications of images are rapidly increasing. Image related services and software such as web-based image presentation and image manipulation for personal or commercial purpose enable users to view contents of remote image archive and to manipulate enormous amount of images in local or network based storage as well. It is necessary for users to identify the images efficiently so that the same images are perceived as one physical entity instead of recognizing them as different images as the trends are getting stronger. In order to support this environment, we propose a method that generates image identifiers or indexing for images within a solid and efficient manner. The proposed image identifier utilizes multiple index values. The integration of component index values creates a unique composite value that can be used as a file name, file system identifier, or database index. Our experimental results on generation of constituent index values have shown favorable results.

• Journal of Home Health Care Nursing
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• v.25 no.3
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• pp.191-203
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• 2018

Purpose: The purpose of this study is to investigate the problems experienced by Korean adolescents due to smartphone use and related factors. Methods: The subjects were 57,463 middle school and high school students nationwide as the 13th The Korea Youth Risk Behavior Web-Based Survey, 2017. Data were analyzed using means, standard deviations, and composite sample multiple logistic regression analysis. Results: This study confirms that negative experiences were related to general characteristics of sex, academic background, socio-economic level, residential area and form, academic performance, and parental education; health behavior characteristics of subjective health cognition, depression, and stress; and characteristics related to smartphone use. Smartphone usage time amd use of services were factors related to the problems experienced from using smartphones. Conclusion: Based on the results of this study, it is necessary to plan for the use of smartphones in consideration of physical and emotional health and to educate adolescents to promote social communication between family and friends. It is also necessary to develop and apply a smartphone use management program to maintain a balance between smartphone use, academic performance, and school life.

• Journal of Digital Contents Society
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• v.11 no.3
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• pp.393-398
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• 2010

Composite and compact devices equipped with the functionality of digital still image acquisition, such as cellular phones and MP3 players are widely available to common users. In addition, the application of digital still images is becoming common among security and digital recording devices. The amount of still images, that are maintained or shared in personal storage or massive storage provided by various web services, are rapidly increasing. These still images are bound with file names or identifiers that are provided arbitrarily by users or that are generated from device specific naming method. However, those identifiers are vulnerable for unexpected changing or eliminating so that it becomes a problem in still image search or management. In this paper, we propose a method for still image identifier generation that is created from the still image internal information.

• The KIPS Transactions:PartA
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• v.9A no.4
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• pp.603-614
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• 2002

Delivery efficiency of e-learning media can be influenced by authoring processes. Generally, a moving picture recorded by video camera can be delivered to student by multimedia streaming service, using media server technology. A e-learning media authored by lecture authoring tool is played in a student application by download-based delivery system. Recently, some animation know-how are applied to author e-learning media by hand-operation. In this paper, we suggest a client-based streaming service for the e-leaning media consists of media files and integration data The lecture of e-learning media nay be divided into some time-based small blocks. Each blocks can be located distributed site. The student system gather those blocks by download-scheduling. This is a valid method for QoS guarantee streaming services. In addition to our study, lecturers can author composite e-learning media includes media files and dynamic web pages simply, The distributed e-learning media files of our study is managed by multi-author and updated rapidly.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.