• Journal of Software Assessment and Valuation
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• v.14 no.1
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• pp.9-17
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• 2018

In many respects, the use and importance of open source software in companies and individuals are increasing as the days pass. However, software evaluation for users, software classification of filtering fundamentals research can not deal flexibly according to the characteristics of open source software. They are using a fixed classification system. In this research, we provide a classification model of open source software that can flexibly deal with the classification of open source software and the software category of new open source software.

• Journal of Software Engineering Society
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• v.25 no.1
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• pp.1-9
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• 2012

RFID systems have been widely used in various fields such as logistics, distribution, food, security, traffic and others. A RFID middleware, one of the key components of the RFID system, perform an important role in many functions such as filtering, grouping, reporting tag data according to given user specifications and so on. However, manual test data generation is very hard because the inputs of the RFID middleware are generated according to the RFID middleware standards and complex encoding rules. To solve this problem, in this paper, we propose a black box test technique based on RFID middleware standards. Firstly, we define ten types of input conversion rules to generate new test data from existing test data based on the standard specifications. And then, using these input conversion rules, we generate various additional test data automatically. To validate the effectiveness of generated test data, we measure coverage of generated test data on actual RFID middleware. The results show that our test data achieve 78% statement coverage and 58% branch coverage in the classes of filtering and grouping, 79% statement coverage and 64% branch coverage in the classes of reporting.

• Journal of KIISE:Software and Applications
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• v.32 no.9
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• pp.901-911
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• 2005

With the development of e-commerce and the proliferation of easily accessible information, information filtering has become a popular technique to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. While many collaborative filtering systems have succeeded in capturing the similarities among users or items based on ratings to provide good recommendations, there are still some challenges for them to be more efficient, especially the user bias problem, non-transitive association problem and cold start problem. Those three problems impede us to capture more accurate similarities among users or items. In this paper, we provide probabilistic model approaches for UCHM and ICHM which are suggested to solve the addressed problems in hopes of achieving better performance. In this probabilistic model, objects (users or items) are classified into groups and predictions are made for users considering the Gaussian distribution of user ratings. Experiments on a real-word data set illustrate that our proposed approach is comparable with others.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.6
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• pp.253-260
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• 2015

When we buy items in online stores, it is common to face recommended items that meet our interest. These recommendation system help users not only to find out related items, but also find new things that may interest users. Recommendation system has been widely studied and various models has been suggested such as, collaborative filtering and content-based filtering. Though collaborative filtering shows good performance for predicting users preference, there are some conditions where collaborative filtering cannot be applied. Sparsity in user data causes problems in comparing users. Systems which are newly starting or companies having small number of users are also hard to apply collaborative filtering. Content-based filtering should be used to support this conditions, but content-based filtering has some drawbacks and weakness which are tendency of recommending similar items, and keeping history of a user makes recommendation simple and not able to follow up users preference changes. To overcome this drawbacks and limitations, we suggest weighted window assisted user history based recommendation system, which captures user's purchase patterns and applies them to window weight adjustment. The system is capable of following current preference of a user, removing useless recommendation and suggesting items which cannot be simply found by users. To examine the performance under user and data sparsity environment, we applied data from start-up trading company. Through the experiments, we evaluate the operation of the proposed recommendation system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1091-1110
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• 2016

Hybrid deployment under current cloud data centers is a combination of online and offline services, which improves the utilization of the cluster resources. However, the performance of the cluster is often affected by the online services in the hybrid deployment environment. To improve the response time of online service (e.g. search engine), an effective scheduling algorithm based on Storm is proposed. At the component level, the algorithm dispatches the component with more influence to the optimal performance node. Inside the component, a reasonable resource allocation strategy is used. By searching the compressed index first and then filtering the complete index, the execution speed of the component is improved with similar accuracy. Experiments show that our algorithm can guarantee search accuracy of 95.94%, while increasing the response speed by 68.03%.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.543-546
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• 2021

최근 온라인 매체에서 무분별한 비속어나 욕설 사용이 늘어남에 따라 유해한 텍스트를 자동으로 필터링하는 시스템의 필요성이 증가하고 있다. 유해 텍스트 필터링 관련 기존의 접근방법은 채팅 프로그램 등 특정 프로그램에 한하여 적용이 되거나 특정 포탈의 웹페이지에 국한되어 적용이 되는 한계가 있다. 따라서 본 연구에서는 AI를 활용하여 모든 웹 페이지의 유해 텍스트를 필터링할 수 있는 Chrome Extension을 구현하고 그 유효성을 검증한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.701-702
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• 2023

코로나 진행중에 오른 집값들과 월세는 학생들 또는 직장인들에게 크나큰 부담으로 다가오고 있다. 따라서 본 논문에서는 '구해줘 룸메즈' 플랫폼을 통하여 사용자들이 경제적 부담을 룸메이트와 나누며 추가로 성향 분석을 사용해 제약사항을 줄여주려 한다. User-based 협업 필터링의 문제점을 보완하고자 본 논문에서는 Item-based 협업 필터링을 통한 방식을 제안한다. 본 논문은 많은 2,30 대 청년들의 자취 혹은 독립 생활에 대한 금전적 부담감을 덜어줄 것으로 기대한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1163-1165
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• 2015

최근 들어, 사용자의 선호도를 고려한 음악추천 시스템의 연구가 활발히 진행되고 있다. 대부분의 음악 추천 시스템은 사용자가 들었던 곡을 분석하여 유사한 노래를 추천하는 시스템을 사용하여 비슷한 성향에서 벗어나지 못한 추천으로 다양한 사용자의 선호도를 만족시키는데 한계가 있었다. 본 논문에서는 개인 정보인 성별, 나이, 지역, 계절, 장르에 가중치를 활용하여 각각의 개인에 가장 알맞은 음악 추천 시스템을 설계하고 구현한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1166-1169
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• 2015

최근 전자제품들이 많이 출시되고 있다. 전자제품이 많고 특징도 매우 다양하며, 사용법도 복잡하다. 이런 이유로 쇼핑몰에서 전자 제품들을 직접 고르기는 무척이나 힘들다. 그래서 사용자가 쇼핑몰에 들어갔을 때 사용자의 성향에 따라 사용자한테 가장 알맞은 전자 제품들을 추천받고 싶다. 사용자의 성향을 나이, 성별, 지역, 소득기준, 외제/국산에 따라 협업 필터링 방법으로 전자 제품을 추천하는 시스템을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1173-1176
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• 2015

최근 한국의 드라마가 국내뿐만 아니라 국외에서도 인기를 끌고 있다. 이로 인해 지상파 채널을 비롯해 종편 채널과 많은 케이블채널에서 전보다 많은 드라마가 등장하고 있으며 드라마 다시보기 기능을 통해 이미 종영되었거나 보지 못했던 드라마를 다시 볼 수 있게 되었다. 본 논문은 사용자의 개인화요소를 반영하여 방영되었던 많은 드라마 중 사용자들에게 가장 적합한 드라마를 추천해주는 추천 시스템을 제안한다.